The Canon 6048B001 PowerShot ELPH 110 HS 16.1MP Compact Digital Camera, in pink, is slim and curvy and a natural fit for your hand. Easy operability and reliable high-quality performance are reason enough to carry it everywhere, but you will also love the attention-getting quality of the camera's distinctly sophisticated modern design. The matte finish projects your taste and style like a fashion accessory, but the PowerShot ELPH 110 HS has its serious side too. A 16.1 Megapixel high-sensitivity sensor plus Canon's DIGIC 5 Image Processor deliver impeccable image quality and high-performance features never before possible. Experience the Face ID feature that recognizes and optimizes images of your friends and family, Intelligent IS that creates sharp, steady shots in a wide variety of situations, and stunning Full HD Video. Smart AUTO lives up to its name, letting you shoot anywhere, anytime as the mood strikes, confident that the camera will automatically make your images the best they can be.
Canon's 4344B001 PowerShot SX30 IS 14.1MP Compact Digital Camera has got you prepared for your next extreme photo opportunity. This point-and-shoot digital camera comes with a massive 35x wide-angle (24-840mm) optical zoom lens - a first for any PowerShot. It also comes with a 2.7-inch Vari-angle LCD, 14.1 megapixels, Zoom Framing Assist and much more. Shoot superb 720p HD video with stereo sound and then view them with an HDMI output connector for easy playback on your HDTV.
The Canon 5251B001 PowerShot SX40 HS 12.1MP Black Compact Digital Camera is a versatile compact point-and-shoot camera that easily captures amazing photos and videos. The HS SYSTEM boosts low-light performance with the DIGIC 5 Image Processor and a high-sensitivity 12.1 Megapixel CMOS sensor. The 35x Optical Zoom offers incredible reach and range, allowing you to shoot any scene, near or far. The camera also features a 2.7-inch Vari-angle LCD for great shooting flexibility. Packed with an array of powerful technologies, the PowerShot SX40 HS is a convenient digital camera capable of brilliant image capture.Built-in Flash Yes: Auto, Auto/Red-eye Reduction, FE Lock, Flash On, Off, Red-eye Reduction, Slow Sync.Effective Flash Range Wide: 1.6 - 23' (0.49 - 7.01 m),Telephoto: 4.6 - 9.8' (1.40 - 2.99 m).What's in the Box: PowerShot SX40 HS Body, Battery Pack NB-10L, Battery Charger CB-2LC, Lens Cap, Case for Hot Shoe Cap, Neck Strap NS-DC11, Interface Cable IFC-400PCU and Digital Camera Solution CD-ROM.
The Canon 5096B001 PowerShot ELPH 300 HS Black 12MP Digital Camera with 5x Zoom and 2.7 In. LCD Display has a slim profile that will not only make you stand out from the crowd, but it will also deliver incredibly brilliant still images and amazing 1080p HD videos. Once you turn on the camera, be prepared to be amazed at how much is packed into one of the slimmest point-and-shoot digital cameras on the market. So let your imagination run free and let the HS SYSTEM, 24mm 5x Optical Zoom and 12.1 Megapixels help you achieve great images everywhere.
Premium picture quality and superb performance come easy with this comfortable to handle digital SLR. At a time when we want complex things to become simple, the EVOLT E-500 succeeds in doing so with a sleek, lightweight design for impressive portability and advanced controls and options that can be accessed with minimal effort. Bursting with speed and producing spotless pictures with exceptional color and detail, the EVOLT E-500 is tailor-made for anyone to use while capturing the imagination in the process. Features: 8 megapixel full frame transfer CCD Patented Dust Reduction System uses the Supersonic Wave Filter (SWF) to loosen and remove any and all dust and debris from the image sensor. Large, high-resolution 2.5 LCD 25 shooting modes All digital lenses Dual media slots Automatic pop-up flash 7 available flash modes Stable and lightweight body with ergonomic styling and an advanced grip PictBridge-compatible Select from left, center, or right Auto Focus points either automatically or manually AF Illuminator to focus in dim light or darkness Shoot using any of five focus modes Exclusive one-touch White Balance button Eliminate unwanted image noise with Noise Reduction Five popular Monochrome modes Choose from sRGB or Adobe RGB color spaces Contrast, Sharpness and Tone Controls. Select the format that best suits your needs: JPEG, RAW, JPEG RAW, TIFF Package Includes: EVOLT E-500 body 14-45mm f/3.5-5.6 lens 40-150mm f/3.5 - 4.5 lens USB Cable Video Cable Li-Ion Battery Pack (BLM-1) Li-Ion Battery Charger (BCM-2) Shoulder Strap OLYMPUS Master Editing Software CD-ROM Manuals Warranty Card
Fujifilm FinePix S4200.What's in the box: Fujifilm FinePix S4200 Digital Camera (Black), 4 x AA Type Alkaline Batteries, Shoulder Strap, USB-A/V Cable, Lens Cap and Lens Cap Cord, CD-ROM and 1-Year Limited Warranty.
See the world in 20.3 Megapixels
Nikon's affordable, compact and lightweight D3100 digital SLR features a high-resolution 14-megapixel CMOS DX sensor, high-quality 3x NIKKOR 18-55mm VR image stabilization zoom lens and intuitive onboard assistance including the learn-as-you grow Guide Mode.Capture beautiful pictures and amazing Full HD 1080p movies with sound and full-time autofocus. Easily capture the action other cameras miss with a fast start up time, split-second shutter release, 3 frames per second shooting and 11-point autofocus.Lens: 5-Year Warranty (1-Year International 4-Year USA Extension).


3X Motion will be at SPS 2019 in Nuremberg
3X Motion will be at SPS 2019 in Nuremberg Germany.
SPS: Smart production solution exhibition
Time: 26th - 28th November, 2019
Booth: Hall 3 - Stand 554
Product display: Stepper motor, Brushless DC motor, Brushed DC motor, Servo motor, PM motor, segment stators, segment servo motor, IP67 water proof motor and motor applications


Photovoltaic solar panel converts solar rays into electrical energy
Want to learn more about solar panels? Understand their types, functions and advantages
Have you thought about a more sustainable way to obtain energy? One of the alternative and renewable sources that has been growing and gaining more and more space among Brazilians is solar. Brazil is an excellent market for the energy sector, as the average solar radiation that falls on the country's surface is up to 2300 kilowatt-hours per square meter (kWh/m²), according to Cepel's Solarimetric Atlas.
Despite some incentives for the use of this type of renewable energy (important as it allows for a reduction in concerns regarding the reservoirs of hydroelectric plants, which in recent years have suffered from the lack of rain and excessive sunshine), they can still be observed some doubts among consumers and those interested in applying this system in their homes or in their businesses. How does it work? What is the cost of its installation? Is the financial return beneficial? Where to buy? The questions are many. Well, let's get to the answers!
A photovoltaic solar energy system (or “solar energy system” or even “photovoltaic system”) is a model in which the components of your kit work in order to capture solar energy and convert it into electricity. Learn more in the article: "What is solar energy and how does the process of generating electricity via solar radiation work?".
The energy produced can then be used to supply the electricity grid on a large scale, such as in solar plants (commercial energy sector), but it can also be generated on smaller, residential scales (solar energy for domestic use). In addition to the solar system for generating electricity, there is also one for thermal energy, which aims to use solar radiation to heat water.
Photovoltaic solar energy systems have some basic components, grouped into three different blocks: the generator block, the power conditioning block and the storage block. Each group is made up of components with specific functions.
- Generator block: solar panel; cables; support structure.
- Power conditioning block: inverters; charge controllers.
- Storage block: batteries.
But in all this, what is a photovoltaic solar panel? It is considered the heart of the systems and is part of the first block, that of energy generation. Its specific function is to convert solar energy into electricity. The number of solar panels needed varies depending on the household's energy demand.
How does the solar panel work?
Solar panels generate electricity from the sun in a very simple way. In addition to photovoltaic energy being considered clean as it does not generate waste beyond the plates and does not cause damage to the environment, the panels that transform sunlight into electrical energy require minimal maintenance. A photovoltaic solar panel is formed by a set of photovoltaic cells that have electrons (negatively charged particles that rotate around the nuclei of atoms) and these, in turn, when hit by solar radiation, move generating an electric current. For this reason, periodic inspections are necessary to check for accumulation of dust, leaves, or other interference (such as bird debris) on the panel . Usually rain is enough to keep the panel free of debris, but when it is not, just clean it with a damp cloth and neutral detergent, always using rubber gloves and checking for loose or oxidized wires (which happens mainly in wetter or salty regions) to avoid accidents.
Size and lifespan
The sizes and weights of solar panels are quite variable. There are many types and variations, but a panel is, on average, approximately one square meter, and weighs just over 10 kilos. A panel of these proportions has about 36 photovoltaic cells, being capable of producing around 17 volts, and a power of up to 140 watts.
Existing models generally range from five to 300 watts of maximum power, depending on the purpose of their use, and the technology adopted. In addition, several photovoltaic panels can be installed, which can be arranged in different ways, allowing you to work with many variations of solar energy systems. A solar panel has a useful life of approximately 25 years, being very practical as it does not need heavy maintenance (remembering that other system components can have a longer or shorter lifespan compared to this one). The return on investment time in the photovoltaic system is variable, and depends on the amount of energy the property requires. Despite this, the advantage of the home system is the economy: once this payback time is reached, the energy bill no longer needs to be paid. Solar energy that turns into “free” electricity! A lot of money can end up in savings rather than being spent without much benefit.
There are three basic types of photovoltaic solar panels
Monocrystalline solar panel
They have a high yield, and are made of monocrystalline silicon cells, that is, each cell is formed by a single crystal of this element. The manufacturing process of these panels is complex, as it requires the production of single crystals of high purity silicon for each photovoltaic cell.
Polycrystalline solar panel
Less efficient than the previous panel; in polycrystalline, cells are made up of several crystals, not just one. The end result is a photovoltaic cell that looks like broken glass.
thin film panel
The photovoltaic material is deposited directly on a surface (which can be metal or glass) to form the panel. Despite being cheaper, they have a much lower energy efficiency, making it necessary a much larger area to compensate.
How to choose?
The choice of the type and number of panels to be installed depends on several aspects, such as:
- Energy demand;
- Purpose of energy use;
- System installation location ;
- Available space.
Where to install?
Residential solar panels are usually installed on roofs (rooftop), however, you should be aware of some recommendations:
- The generation of electricity by solar panels can be harmed by winds, shadows and reflective surfaces, which interfere, reducing the efficiency of the process;
- It is important that there is good air circulation in the place so that the cells do not overheat;
- The roof must be resistant to the weight of the panels.
The tilt and orientation of the panels can also interfere with their efficiency. In the case of Brazil, located in the Earth's southern hemisphere, the installed solar panel must face true north (which is not the same north as given by the compass). For northern hemisphere countries, the solar panel must be facing true south. Magnetic north, where a standard compass points, is aligned with the Earth's poles and is constantly moving, albeit light. True north is what you see on a paper map and is constant.See more about installing solar energy in your home. See also the video on how the solar panel works (in English).
Remember to ensure that the components used are certified by the National Institute of Metrology, Quality and Technology (Inmetro), which implemented Ordinance No. 357 in 2014, with the objective of establishing rules for the generation equipment. Photovoltaics .
Solar energy is one of the most promising renewable resources in Brazil and in the world, as it causes minimal environmental impacts and reduces consumers' carbon footprint - they will be minimizing their emissions by choosing a way of obtaining energy with low harmful potential.
Unfortunately, there are still few incentives and financing lines for this type of energy in Brazil, which are still difficult to access and have little applicability. It is expected that, with the increase in the consumption of photovoltaic energy systems, new incentives will emerge, more applicable and accessible to common housing.
Although the use of solar energy is considered clean, the use of solar panels can be problematic. This is because, in addition to being expensive, specialists are already concerned about what will be done with thousands of solar panels at the end of their useful life.
Japan's Ministry of Environment has warned that by 2040, the country will produce 800,000 tons of solar waste.
The International Renewable Energy Agency estimated that by 2050 the number of solar panels will be 75 million, weighing 250,000 tons. Which can be a big problem in terms of solid waste, according to scientists.
How are solar panels recycled?
Recycling solar panels is not yet economically viable. Although they have valuable materials such as copper and silver, they are not worth as much as cell phones and other devices.
Because of this, the Electric Energy Research Institute claims that the best solution for the fate of solar panels, for now, is landfill.
However, in the state of Washington, in the United States, legislation was passed that requires solar panel manufacturers to have a recycling plan for their products.
In Europe, the first solar panel recycling plant was inaugurated in June 2018.


Objective: The basic objective of the system is to provide the service to transfer balance amount from any mobile network to other mobile network. For example the system will provide the service if a UFONE user wants to share his mobile balance to TELENOR number. In simple words share mobile balance among cross networks.
There are 5 mobiles having connection of UFONE, TELENOR, JAZZ, ZONG and WARID attached to the server machine.
The following steps show the flow of process:
Step 1: User will send amount to server’s mobile. (Mobile that is attached to the server). Step 2: After that he will send the destination mobile number. Step 3: The received amount and destination mobile number is transfer from attached mobile to server. Step 4: Sever will parse the message to find out how much amount has been received and, on which mobile network we have to transfer. Step 5: Sever will automatically send amount to destination. Step 6: Now the system will send the Acknowledgements on both side to sender and receiver.
Example: Consider the case UFONE user want to share his mobile balance to telenor number.
Ufone user type the number, amount he wants to share and our ufone server's mobile number. Amount and number will received on UFONE mobile which is attached with server. Message is parse by the server to find out the destination number and amount to be transfer. Then server will find it is telenor number so the amount from telenor mobile will be send to the destination number. Now the message is send to the sender that we have transfer your amount to the desire number. And send acknowledgement to receiver that you have receive the amount from xyz.
Analysis of System:
Security: The system must be secure for amount transfer.


KIGALI, Rwanda, 21 January 2019, -/African Media Agency (AMA) /- Today, the African Institute for Mathematical Sciences (AIMS) launched applications for the second intake of its unique African Master’s in Machine Intelligence (AMMI), a novel one-year intensive foundational master’s program that will provide brilliant young Africans with state-of-the-art training in Machine Learning (ML) and its applications.
“We were encouraged by the enthusiasm and excellent applications we have received for the first intake last year. The AMMI program gives students intensive mathematics and Artificial Intelligence (AI) and machine learning training, preparing them to think of new ways to solve local and global challenges. The first cohort has really demonstrated the potential for foundational AI and machine learning research, and will enrich the machine intelligence ecosystem on the continent. We look forward to accepting new applications for the program,” said Professor Moustapha Cissé, Director of the AMMI Program at AIMS and Head of the Google AI center in Ghana.
The first cohort of AMMI students, supported by Facebook and Google, and based at the AIMS Rwanda campus in Kigali, are set to graduate in June this year. The AMMI program includes 30 students from 10 African countries with 43 percent women. After the one-year intensive program, graduates are encouraged to pursue doctorate study or join the best industrial and public R&D labs, in Africa and beyond.
“Creating an effective, globally connected community of MI practitioners in Africa will reduce the technology gap, strengthen Africa’s economies and enable better governance.” said Professor Cissé.
To apply, students must have a Bachelor’s degree in mathematics, computer science, electrical engineering or an equivalent from an accredited institution, be interested and/or have background in artificial intelligence, machine learning, as well as have a strong interest in solving national or continental problems using science and technology. Visit aimsammi.org for more details.
Distributed by African Media Agency (AMA) on behalf of African Institute for Mathematical Sciences (AIMS)
Founded in 2003, the African Institute for Mathematical Sciences (AIMS) is a Pan-African network of centres of excellence for post-graduate training, research and public engagement in mathematical sciences. The AIMS network has six centres of excellence teaching a Masters in Mathematical Sciences, including a co-operative option with a direct link to industry in three centres. Currently, the network has over 1600 alumni from 43 African countries.
The network also includes research centres and programs with 90 researchers including six prestigious Chairs across the network. In Cameroon and Rwanda, AIMS runs a gender-responsive teacher training program, in addition to two critical initiatives: Quantum Leap Africa to prepare Africa for the coming quantum revolution, and the Next Einstein Forum to propel Africa on to the global scientific stage.
In September 2018, AIMS launched the African Masters in Machine Intelligence at its Rwanda campus. In partnership with Facebook and Google, AMMI’s goal is to bring the best of AI education in Africa where AMMI students interact with world-class lecturers to grow their technical skills to respond to both present and future needs of Africa and the world.
Director, Communications & Public Engagement
AIMS Global Network
E : firstname.lastname@example.org
Source : http://amediaagency.com/aims-launches-applications-for-second-intake-of-african-masters-in-machine-intelligence/


The overall governance of IEEE-HKN is the responsibility of the Board of Governors, a volunteer organization of IEEE-HKN members that have prominent positions in academia and industry. The Board consists of a president, president-elect, past president, and treasurer, each of whom serves a one-year term. Regional Governors serve the IEEE-HKN’s geographic regions (now aligned with the IEEE Regions), and four at-large members each serve one three-year term. There is a pair of student governors who serve one-year terms.
2021 IEEE-HKN Board of Governors
I was inducted into HKN shortly before graduation and didn’t have much involvement in the chapter. But, there was something about being selected for induction that stuck with me. Many years later I was asked if I could help HKN by becoming Treasurer and I couldn’t say no, I wanted to give back to this organization. We have had the opportunity to make progress financially in supporting the mission of HKN. I’ve now had the opportunity to attend 3 Student Leadership Conferences and I’ve been very impressed with the students who have attended and the program that they developed. Absolutely makes me want to continue working and supporting HKN.
James M. Conrad
James M. Conrad is a professor at the University of North Carolina at Charlotte. He has also worked at IBM, Ericsson/Sony Ericsson, and BPM Technology. Dr. Conrad is a Professional Engineer, a Senior Member of the IEEE and a Certified Project Management Professional (PMP). He will serve as IEEE-HKN President in 2022. He previously served on the IEEE Board of Directors as IEEE Region 3 Director in 2016-2017, and as IEEE-USA President in 2020.
Edward A Rezek
Edward A. Rezek received a BS degree in Electrical Engineering and an AB degree in Physics from Washington University in St. Louis, MO in 1976; he received MS and PhD degrees in Electrical Engineering from the University of Illinois, Urbana-Champaign, IL in 1977 and 1980. He retired from Northrop Grumman Space Technology (formerly TRW Space & Electronics Group), an entity specializing in electronics development for US Government and commercial applications after 35 years. His work experience has ranged from basic R&D to manufacturing and has covered the spectrum from advanced technology development for US Government space applications to manufacturing low cost components for commercial applications. He has received 19 patents and has >50 publications in refereed journals. He is a recipient of the TRW Chairman’s Award for Innovation and a Northrop Grumman Distinguished Innovator Award. He is an IEEE Fellow for qualification of GaAs microelectronics for space applications.
Dr. Jason K. Hui is a Senior Staff Program Engineering Manager at Elbit Systems of America. He has been in the aerospace and defense industry for over 17 years with primary interests in systems engineering and technology and engineering management. He received the B.S. and M.S. degrees in Electrical Engineering, and the Ph.D. degree in Mechanical Engineering, all from UCLA. He also received the M.S. degree in Systems Engineering at the Johns Hopkins University. Dr. Hui previously worked at BAE Systems, Draper, and The Aerospace Corporation. He is an IEEE Senior Member, AIAA Associate Fellow, and a certified Project Management Professional. He has received several awards over the course of his professional career including the IEEE New Hampshire Section’s Outstanding Young Engineer of the Year Award in 2010, the AIAA Sustained Service Award in 2013, and the PMI New Hampshire Chapter Individual of the Year Award in 2016.
Michael L. Benson is a PhD Candidate at the University of Michigan’s Radiation Laboratory and a research scientist at Maxar. He is a co-advisor to the Beta Epsilon Chapter of HKN, was the IEEE-HKN Student Governor in 2017 and 2018, and has served as the IEEE-HKN Governor for Regions 3 and 4 since 2019. He additionally serves as the chair of the IEEE-HKN Membership & Chapters Committee. His research interests include multi-modal remote sensing and environmental modeling.
Christopher Sanderson is an Organizational Leadership Effectiveness coach and keynote speaker. He is an US Army Veteran with multiple Honorable Tours of Duty and numerous corporate and non-profit recognitions. During his time in the military, he lived by the motto “Mission First, People Always!” He is passionate about emboldening people to live and lead better in a better #VUCA (Volatility, Uncertainty, Complexity, and Ambiguity) world. As a manager, consultant, speaker, coach and leadership facilitator, he is been fortunate to be a part of some amazing organizations in the military, corporations and non-profit teams from around the world in helping them make better decisions and even braver actions to amplify their impact. As the IEEE Region 5 South Area Chair and Houston Section Chair, he has been given the opportunity to support the future leaders that will be “Advancing Technology for Humanity” today and tomorrow.
Lorena Garcia received her Electronic Engineering degree from Universidad del Norte (2006) and the M.Sc. in Electronic and Computer Engineering from Universidad de Los Andes (2008). She has more than 10 years of experience in academic administration, teaching and research in important institutions in Colombia like Universidad de Los Andes, Universidad Militar Nueva Granada, and Pontificia Universidad Javeriana. Prof. Garcia has been Associate Dean of Electronic Engineering and Systems and Telecommunications Engineering programs at Universidad Sergio Arboleda, and Business Director of the Center of Excellence and Adoption on Internet of Things. She has also been a consultant on design and certification of internal telecommunications networks. Currently, she is Professor and Director of Laboratories and Facilities of the Faculty of Engineering and Basic Sciences of Universidad Central.
Lorena has served as an IEEE volunteer for 17 years.
Professor Garcia was inducted to the HKN Eta Chapter in 2016. During 2019, she has served as an HKN Globalization Committee Member.
Dr. Preeti Bajaj is an Electronics Engineer Graduated in 1991, Post graduate in 1998 and doctorate in Electronics Engineering in 2004. Having 27 year of experience, currently she is Director of G.H. Raisoni College of Engineering, Nagpur with 5500 students. She is known for her strategic planning, project management, innovations and out of box thinking. Her research interest includes Intelligent Transportation System, Soft Computing, Hybrid Intelligent Systems & Applications of Fuzzy logic in ITS. Her professional society affiliation includes Fellow- Institute of Engineers, Fellow IETE, Senior Member- IEEE. She is presently Secretary-IEEE India Council. She was also on Standing Committee of Global accreditation of IEEE Education Board. Six Doctoral students have been awarded PhD, 3 have submitted and 4 are pursuing & 30 have done Masters under her.
Her major contribution is in design and development of driver assistance systems like drivers fatigue detection system, driver’s action and emotion detection system, driver’s behavior modeling and applying hybrid fuzzy /genetic/PSO to various Intelligent Transportation Systems. She has also been instrumental to design and development of ITS systems in public and private sector on various areas of specialization like toll collection/toll audit system/highway traffic management system and advanced traveler Information systems.
She is founder General Chair of ICETET series of Seven IEEE Conferences. Dr. Bajaj holds awards like Best Teacher award, “Bhaskar Woman’s Award” for year 2010 and “Faculty of Month”. She is resource person for creating awareness of Outcome Based Education and also is Ex-member of General Council of NBA. Her current efforts include thrust on Innovations, internships, and entrepreneurships. Strong belief in innovations, she is putting all efforts to create an ecosystem for innovations and entrepreneurship. Her efforts have turned 208 projects into patents leading to transform a private Engineering institution into India’s premier, Engineering and Research Institute in last 5 years.
She is very Active IEEE Volunteer for last 18 plus, Worked regional, council, section level, subsection, branch level, above all counselor for regional and students group for IEEE activities and taking them to wonderful world of innovations through IEEE. Her passion is to connect bringing industry institutions and community at one place and brining need-based innovations through technology for students by students.
M. Ryan Bales
Dr. M. Ryan Bales is a Senior Research Engineer in the Sensors and Electromagnetic Applications Lab (SEAL) at the Georgia Tech Research Institute (GTRI). He specializes in FPGA and embedded system design, and real-time signal processing for electronic warfare applications. His work has been the subject of 19 scholarly refereed publications, and he is an IEEE Senior Member and a professional engineer licensed in the state of Georgia.
Dr. Bales is passionate about community and professional service. He has served as treasurer of the Atlanta IEEE Aerospace and Electronic Systems Society / Geoscience and Remote Sensing Society joint chapters since 2013, and is engaged with the leadership of the Atlanta Section. He is a member of the Association of Old Crows, Eta Kappa Nu, and the Order of the Engineer. He was a very active member of the Gamma Theta chapter of Eta Kappa Nu for four years as an undergraduate and graduate student. He received the Gamma Theta Advisor’s Leadership Award and the Founder’s Award for Service for his work in these roles. He continues involvement with HKN today, having served on the Professional Membership Committee since 2017. At GTRI, he represents SEAL on the GTRI Faculty Senate for the 2017-2020 term, acting as a liaison between over 220 SEAL research faculty and the Georgia Tech governing body. In 2018, he helped organize a petition to increase GTRI representation on the Georgia Tech Presidential Search Committee. He regularly volunteers in K-12 and collegiate STEM activities. Since 2006 he has served as a judge at more than 30 local, regional, and state science fairs in three states. Dr. Bales is certified by the state of Georgia to test and report water quality in Georgia watersheds, and collects data on the Etowah River.
Dr. Bales received his Bachelor of Science degree in Computer Engineering as an Honors Academy Fellow in 2004 and his Master of Science degree in Electrical Engineering in 2006, both from the University of Missouri – Rolla. He received his Doctor of Philosophy degree in Electrical Engineering from the Georgia Institute of Technology in 2011.
Hulya Kirkici is Professor and the Department Chair of Electrical and Computer Engineering at the University of South Alabama. She received B.S. and M.S. in physics from Middle East Technical University (METU), Turkey; and Ph.D. in electrical engineering from Polytechnic University (currently NYU), NY. Previously, Dr. Kirkici was Professor of electrical and computer engineering at Auburn University (1992-2016), visiting scholar and Summer Faculty Fellow at the Air Force Research Laboratory – Wright Patterson Air Force Base, (2014-2015), and visiting scientist/engineer at NASA, Marshall Space Flight Center, Huntsville, AL (1999-2000).
Dr. Kirkici is a Fellow of IEEE for “contributions to high frequency, high field dielectric breakdown and electrical insulation for space and aerospace power systems” and a recipient of the IEEE Eric O. Forster Distinguished Service Award and the IEEE William G. Dunbar Award (High Voltage Technical award). Dr. Kirkici served as 2019 IEEE Vice President – Publications, IEEE Director and was a Member of the IEEE Board of Directors and the Chair of Publications Services and Products Board. She was the President of IEEE Dielectrics and Electrical Insulation Society, the Vice President of IEEE Sensors Council (2014-2015), and Treasurer and Finance Chair of IEEE PSPB (2011-2014)
Sean Haynes graduated from Northeastern University in Boston, MA in 1991 with a Bachelors of Science in Electrical Engineering. While at Northeastern University, Sean was inducted into the Gamma Beta Chapter of HKN and was Secretary of IEEE Student Chapter. As a Co-op Student Sean worked as a Software Tester and Software Developer for General Electric Automated Systems Department in Burlington, MA. After graduation Sean obtained a job with General Dynamics Electric Boat Division in Newport, RI as Systems Tester. In 1993, Sean started his Master’s Study at the University of Massachusetts in Dartmouth, MA. In 1994, Sean relocated to Charlottesville, VA to start a job with Avionics Specialties, Inc. working as Software Developer on embedded avionics software. Sean continued his Master’s study at the University of Virginia in Charlottesville and graduated in 1996 with a Masters of Engineering in Electrical Engineering. In 1998, Sean started work at Litton Marine Systems, now Northrop Grumman. Sean has been employed with Northrop Grumman since 1998 and now works at Undersea Systems in Annapolis, MD. While in Charlottesville, Sean continued his active volunteer work in IEEE as Secretary/Treasurer and eventually Chair of the Central Virginia Section. He is program chair for the 2020 IEEE-HKN Experience.
Sandro Sartoni studied Electronic Engineering at Università di Firenze, where he received his Bachelor Degree in July 2017 and then moved to Turin, where he received a Master of Science Degree in Embedded Systems in October 2019. He currently is pursuing a Ph.D. in the field of Computer Engineering at Politecnico di Torino. This is second term as Student Governor for IEEE-HKN.
Sandro served as chair of the Social Media Subcommittee of the HKN Board of Governors in 2020, and was essential in the growth and impact of HKN’s social media messaging, platforms and audience. He also mentored the officers of the chapters in Waseda, Japan and the two new Chapters in Spain. He was President of the Mu Nu chapter for the 2018-2019 academic year. That role enabled him to be more involved in international HKN activities and in the beginning of 2019, he became part of the HKN Board’s Public Relations and Communications Committee. In July 2019, he took part at COMPSAC, the IEEE Computer Society annual conference, in which, together with Nancy Ostin, presented what HKN is and what chapters do throughout the world.
Joseph Greene is pursuing a Ph.D. in Computational Imaging at Boston University with the support of a NSF Neurophotonics Research Trainee Fellowship and a BU Nanotechnology Innovation Center Cross-Disciplinary Fellowship. He earned a Bachelor’s degree in Electrical Engineering in 2018 and a Master’s degree in Electrical and Computer Engineering in 2019 from Boston University. He was inducted into the Kappa Sigma Chapter in his junior year, and began to see himself as a member of a global community of like-minded engineers dedicated toward changing society on many levels. He served as a member and later as the twice-elected Vice President of Kappa Sigma. He developed and hosted an Arduino microprocessor workshop that taught the basics of coding and electronics to interested students as well as helped organize invited technical talks from Kappa Sigma alumni and professional associates to foster a desire for continual learning in the Chapter. He also organized tutoring sessions around finals.
To see a list of committee members click here


What is Jitter & How to Deal With It?
The quality of data transmission over the Internet affects the quality of our work and leisure time. Therefore, it’s essential to use all the right tools and methods to provide yourself with stable data transfer speeds. Fluctuations and jitter in the network can significantly degrade the quality of the Internet connection, making it impossible to fully use some services.
Many users worldwide experience jitter while making Internet calls, playing online games, or watching videos. This Internet connection problem depends on several factors.
This article will help you learn the concept of network jitter and its causes. Also, valuable information will help eliminate the jitter when you’re connected to the Internet.
What is network jitter?
Jitter is the variation of data latency in an Internet connection. All information on the Internet is transmitted in packets of data with an equal delay. Changing this interval breaks the signal’s integrity and makes it less stable. Measurement variability occurs because of the technical nature of the Internet. Each data packet may take a different route to its destination, the load on different nodes on the network varies over time, and so on.
The technology behind jitter
Streaming data over an Internet connection is transmitted at intervals measured in milliseconds. For example, say data packets 1 and 2 are transmitted at 10-millisecond intervals throughout the connection. This stable indicator allows you to achieve high image quality, and no picture freezes. If the data packets are transmitted at different intervals (for example, first 10 and then 12 milliseconds), the stability is broken, and there is interference. This leads to delays, jitter, and other stability problems with your Internet connection.
Causes of jitter over a network
There are several main causes of jitter over an Internet connection:
- Interference with a wireless Internet connection
- Improper router configuration
- Improper choice of router
- Insufficient speed of Internet connection from provider
- Server failures
- Too much network load on a particular device (router)
Most of these causes of jitter can be corrected at home without spending much money or going to specialists.
Examples of jitter
The most common streaming data jitter occurs in the following user scenarios:
- Online calls via messengers and other services
- Online gaming
- Online viewing of video content in high quality
For example, data latency is often encountered during Internet calls and business conversations via Skype, Zoom, Google Meet, and other similar services. You will immediately notice the jitter, as it is manifested by interference, distortion of sound, audio, and picture freezes.
Accordingly, the greater the delay, the worse the Internet connection becomes. If the situation is not corrected, the problem may recur every time you turn on an online video, start an online game, or call someone over the Internet.
The effects of jitter
Constant jitter harms the quality of the Internet connection. Note that maximum Internet speed and the presence of jitter are not correlated. You can use high-speed Internet, but for some reason, your connection may experience jitter.
Consequently, you may not be able to fully use all the features of the Internet, including everything related to streaming data in real-time. Therefore, it is necessary to detect the cause as soon as possible and eliminate the network jitter effect.
What is acceptable jitter for the Internet?
You probably think that jitter, in any form, is terrible for your Internet connection. However, it is not. Data packet transmission always involves a minimum delay of a few milliseconds. On a stable Internet connection, this delay is always the same, that is, 10 milliseconds for thousands and hundreds of thousands of data packets transmitted. Allowable jitter is up to 30 milliseconds.
How do I fix jitter on the Internet?
There are several proven ways to get rid of high Internet jitter. Use one or more to stabilize data packet latency and improve network quality.
Use an Ethernet cable
One of the main causes of Internet signal jitter is wireless connections on devices with unstable data transmission. Of course, using Wi-Fi signals is very convenient in large houses, offices, and apartments. However, a wireless signal can be interrupted by external factors.
For example, microwave ovens, radio signals, and other factors can interfere with the Wi-Fi signal to your devices. This will cause you to experience constant jitter in your Internet connection, regardless of your ISP’s speed.
Fix this by using an Ethernet cable to connect devices that allow it, for example, a TV, computer, game console, or surveillance system. An Ethernet connection using fiber optic cable is known to be more stable. In 95% of cases, it will reduce or eliminate network jitter.
Packet prioritization is one tool to combat high Internet signal jitter. Each router has a QoS (signal quality) adjustment feature that allows you to prioritize certain data packets manually. This can help if you are experiencing jitter due to traffic congestion or many devices connected to the same router.
It only takes a few minutes to set up the prioritization. However, this can only be done in the router’s web settings interface. Use your device manufacturer’s instructions or contact your ISP’s support team.
Get a powerful router
Investing in a high-quality router always pays off. A powerful device transmits data from the ISP’s network to the user’s devices as quickly as possible without interference.
There are a few key parameters to consider when choosing a router:
- Maximum data rate (it is recommended that it be higher than 1 gigabit per second)
- Transmission frequency (2.4 GHz and 5 GHz to balance the signal between different data streams and devices)
- The number of antennas
Often, the cause of a jittery Internet signal is an insufficiently powerful router that cannot overcome interference and provide stable data transmission at high speeds.
Minimize unnecessary bandwidth usage
There are several ways to minimize the waste of bandwidth on your Internet connection:
- Optimize the process of automatically updating applications and services on all devices connected to your router.
- Limit the use of streaming services while you are working.
- Search for and remove malicious programs and services that stealthily use Internet traffic.
Prioritizing traffic will also help reduce wasteful bandwidth usage.
Check device frequency
Modern routers operate on two frequencies:
- 2.4 GHz
- 5 GHz
It will help if you choose these devices well to minimize network jitter. Two frequencies are needed to distribute traffic between different types of devices. For example, the 5 GHz frequency has less range than the 2.4 GHz. The 2.4 GHz is busier, allowing it to be used for data streaming.
By distributing traffic between different frequencies, you will reduce the load when streaming Internet data.
Use a jitter buffer
In addition to the above preventive measures, a buffer is used to combat jitter. This mechanism is implemented in many VoIP devices. The buffer allows you to compensate for the variable rate of incoming traffic on the receiving side by creating temporary storage for data packets. All incoming packets are sorted according to timestamps and then given to the codec. If traffic is lost, the mechanism re-requests the lost packets.
The buffer size is calculated automatically by the VoIP equipment, or the user sets it forcibly in the settings. It should not be too big, so as not to increase the delivery delay. At the same time, the buffer jitter size shouldn’t be small. Otherwise, it will cause a loss of traffic packets when the delay time changes in the VoIP network.
Jitter vs. Latency
Jitter and latency describe the quality of a network connection and its key parameters. However, the two terms are different. You need to understand the difference between jitter and latency to find the exact cause of a poor Internet connection and fix the problem. Let’s take a closer look at the difference between the two terms.
Jitter is a time delay difference in the transmission of data packets. As we mentioned above, a quality, stable Internet connection requires an exact time difference in the transmission of data packets. Ideally, it should be between 10 and 30 milliseconds. If there is jitter, each data packet is transmitted at a different time interval, making it impossible to achieve a stable streaming Internet signal. This happens most frequently with streaming services, online telephony, online games, and others. On the regular Internet, its effects are insignificant.
Network latency is the time between a user’s action and the response received. In practice, it means the time between a user’s action and a website’s or application’s response to that action, for example, the delay between a user clicking a link to a web page and that web page appearing in the browser.
One of the main causes of network latency is distance, particularly between the client devices fulfilling requests and the servers responding to those requests.
Accordingly, the higher the delay, the worse the Internet connection quality. This parameter is similar to jitter but denotes different processes which you now know about.
Are you experiencing an Internet connection failure? One of the causes could be jitter that is higher than usual. Try to determine the jitter on your device as quickly as possible. If your Internet connection data packet latency is above 30 milliseconds, then you need to apply one of the techniques above to get rid of it.
Our guide will help you identify the causes of jitter and eliminate it as quickly as possible. Don’t let jitter in your Internet connection ruin your gaming or work experience. After all, with unstable data latency, you won’t be able to fully play online games, watch streaming videos, or make VoIP calls with anyone.
Does Jitter Affect Internet Speed?
Data jitter does not affect your Internet connection speed. You can use a data rate higher than 300-500 megabits per second, but you can’t avoid jitter. Therefore, make sure you use the appropriate configuration of network equipment to minimize jitter and achieve the maximum allowable Internet connection speed according to your provider’s plan.
What is an Acceptable Jitter for VoIP?
VoIP (Voice over Internet Protocol) requires minimal jitter for stable, real-time transmission of voice data between users. You need to achieve a minimal jitter of 10-20 milliseconds if you want a stable voice connection without interruptions, distortions, and delays during an online call.
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sihyun.yu at kaist dot ac dot kr / yusihyunc at gmail dot com
I am a Ph.D. student at Algorithmic Intelligence Laboratory @ KAIST, advised by Jinwoo Shin.
Currently, I am working closely with Kihyuk Sohn at Google Research.
Prior to this, I received B.S. in Computer Science and Mathematics (double major) at KAIST under the supervision of Hongseok Yang, who kindly advised me to be a conscientious reseacher.
My research focuses on designing memory-/computation-efficient generative models.
In particular, I am interested in solving this challenge by constructing generative models with various structural assumptions and regularizations in latent space.
Currently, I am actively working on video synthesis, but not limited to; other complex data (e.g., 3D scenes and graphs) and applications are also in my interest.
Besides, I also have an interest in the robustness of neural networks to the distributional shift.
Learning Robust Representations via Nuisance-extended Information Bottleneck
ECCV 2022 Workshop on Out of Distribution Generalization in Computer Vision (OOD-CV)
with Dr. Kihyuk Sohn as Google University Relations (Jul 2022 - present)
NAVER AI Lab
with Dr. Jung-Woo Ha as an external collaborator (Jun 2021 - Sep 2021)
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)
with Prof. Le Song
Prof. Sungsoo Ahn as an external collaborator (Mar 2021 - May 2021)
Korea Advanced Institute of Science and Technology (KAIST)
with Prof. Hongseok Yang (April 2018 - June 2019)
Honors and Awards
Top Reviewer, NeurIPS, 2022
Best Paper Awards, Korean Artificial Intelligence Association, 2021
Recipient, Qualcomm-KAIST Innovation Awards, 2019
Recipient, KAIST Presidental Fellowship, 2018-2020
Recipient, National Presidental Scholarship for Science, 2017-2020
Recipient, Hansung Scholarship for Gifted Students, 2015-2016
RoMA: Robust Model Adaptation for Offline Model-based Optimization
Jun 2022; Samsung Electronics (Suwon, South Korea)
Scaling Video Generation via Implicit Neural Representations
Jun 2022; Pohang University of Science and Technology (Pohang, South Korea)
Abstract Reasoning via Logic-guided Generation
Jun 2021; ICML Workshop on Self-Supervised Learning for Reasoning and Perception 2021 (remote)
Conference reviewer: ICML 2022, NeurIPS 2022, ICLR 2023, CVPR 2023
Workshop reviewer: CVPRW (AI4CC) 2022
Teaching Assistant, Deep Learning (AI502), Fall 2020
Teaching Assistant, Samsung Electronics Device Solutions, AI-Expert Program, Summer 2020
Peer Tutor, System Programming (CS230), Spring 2019
Teaching Assistant, Linear Algebra (MAS212), Spring 2019
Teaching Assistant, TA, Calculus 1 (MAS101), Spring 2018


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CEDRION is a startup of the Business Creation Program of the Universidad Politécnica de Madrid.We are incubated in the ESA BIC Comunidad de Madrid.
Do you want to be part of our team?
If you are entrepreneurial, inquisitive and passionate about technology, email us at firstname.lastname@example.org
CEDRION is incubated in the ESA BIC Comunidad de Madrid. European Space Agency incubation center co-financed by the Community of Madrid and managed by the Madri+d Knowledge Foundation.
Corona Discharge Effect
A corona discharge is an electrical discharge brought on by the ionization of a fluid such as air surrounding a conductor that is electrically charged. Spontaneous corona discharges occur naturally in high-voltage systems unless care is taken to limit the electric field strength. A corona will occur when the strength (potential gradient) of the electric field around a conductor is high enough to form a conductive region, but not high enough to cause electrical breakdown or arcing to nearby objects.


5G+ Beidou positioning
Machine vision system
Ship electric propulsion system
Remote control system
Hull control system
Solar and wind energy complementary charging
01 The extensive use of all aluminum alloys and antibacterial materials, and the use of lithium battery systems in the power system are safe, environmentally friendly and reliable.
02 360-degree panoramic system, you can watch the real-time state of the ship in the water, and the interactive screen enhances the entertainment and interactive experience
03 It can accommodate up to 6 people, and the cockpit can be changed to a room according to customer requirements
04 Automatic driving, remote control, assisted driving, manual driving, can be switched freely
05 Modular design of the whole ship, free assembly and combination.
06 The hard-top design can be modularly assembled to ensure the brightness and wide field of vision inside the cockpit; it can be equipped with windshield, canopy, external lighting and light strips.
All the contents mentioned above have registered patents and protected models. all rights reserved.


One of the earliest computers was the abacus - a counting device using beads. The first modern commercial computer was purchased only 55 years ago. Since, rapid reductions in the cost, physical size, and ever greater increases in speed and memory have heralded what is now a huge annual flow of computers around the world.
As with many imports, the United States imports more than any other territory. In this instance almost five times more net computer imports are received in the United States than in any other territory.


Via Palladio Snc,
Grottaccia di Cingoli, (MC), 62011
Lun-Ven: 9:00-13:00 Lun-Ven: 14:30-18:00
We repair and replace faulty Apple Watch 2nd Gen 42mm batteries. Our trained technicians will replace the Apple Watch 2nd Gen 42mm battery, fully test it and then return your device in excellent working condition.


Coca-Cola is reportedly launching its first phones by collaborating with a major phone brand. Coca-Cola is expected to launch its phone in the first quarter of this year. The leaked images of the phone have surfaced on the internet and people believe that it could be a rebranded Realme 10 4G.
A known tipster Mukul Sharma tweeted that Coca-Cola has confirmed the launch of the phone and he also claimed it will be rolling out in the first quarter only. The name of the brand with which Coca-Cola has collaborated is still a secret yet but if it is the Realme 10 4G then it might feature similar specifications.
Mukul Sharma also shared the renders of the upcoming Coca-Cola phone which shows that the phone has a red back panel with a big Coca-Cola branding on it. It also has two big camera rings and one small LED flash. You can even see two volume buttons on the right side of the phone. If it is going to be the Realme 10 4G then let’s have a look at its specifications.
The Realme 10 4G was launched last year in November as a budget phone. It is powered by the MediaTek Helio G99 SoC, paired with an ARM G57 MC2 GPU. It features a 6.4-inch full-HD+ AMOLED display with 90Hz refresh rate support. The display is protected by the Corning Gorilla Glass 5.
It comes with a 50-megapixel dual camera setup and packs a 5000mAh battery with 33-watt fast charging support. The Realme 10 runs on Android 12OS with Realme UI 3.0. The device features a 16-megapixel selfie shooter.


By Youssef Edward, added August 2019
Laptops today are becoming very necessary due to the highly mobile world. Many people today are seeking for information everywhere and every time when they are in business time or what other times. When buying laptops, many choices exist. This is due to the increasing number od producers. Also each producer provides the market with many specifications for laptops from the physical sense or the storage and processing capabilities sense.
Below are presented useful tips that help when choosing laptops:
1. Processor speed: the processor is the brain of any computing or decision making device. In our case, it is the laptop. When choosing a laptop, one must consider the speed of the laptop. The more the processor speed, the faster the execution of the applications and the programs that will run on the laptop. Many applications require high power processors to be able to be run. This is such as AutoCAD and heavy Java programs. If the person buying the laptop intends to run such heavy program, he must buy a laptop with high speed. The Intel core 2 Duo is the most recommended in the market today.
2. Memory capacity: Memory divides into two types: the run time memory (Known as RAM) and the memory used for data storage (known as hard disk).
The RAM is used for storing data temporarily during data processing it is a short term memory. The RAM is preferable to be larger to speed up the laptop. The larger the RAM size, the more numbers of applications that can be open on the laptop at the same time. If the RAM size is full at any time, the laptop will be hanging or stop and may need for restart. A RAM of 1 GB must be the minimum value used but 2GB RAM is better as the minimum value.
On the other hand, the storage memory which is known as the hard disk is the unit that stores the data when the laptop is off. Any saved files are stored on the hard disk. Therefore, the more the hard disk in size, the more files can be stored. This will depend from user to ser on which size to choose but the sie of 150 GB is the minimum.
3. Screen Size and Resolution: the screen size is the size of the screen expressed in inches. The size choosed for the screen depends from one to one. Many people like smaller size while others like larger laptop screen.
The resolution is a value that affects the screen view. Larger resolution means a better view. Laptops with more resolution are better.
4. Battery life: the larger the batter life, the more reliable the laptop and the mush work can be done on the laptop at a time. Typical values for the battery life are from 2-3 hours but 5 hours battery life can be found in the market. Many laptops come with multiple batteries but at the expense of the cost. If the budget allows, it will worth it because it will give more work time on the laptop.


Generative AI has taken a boom in recent times. With the advent of Foundational models(which are trained on billions of data and parameters and learn to understand patterns) such as Large Language models or LLMs, generative AI has shown beyond creative results. It's amazing to see far AI and machine learning have gone.
Along with this comes the question of the reliability of these models. Does it always show us the correct information? Can I trust such AI systems with critical tasks? What if the AI system generated biased and harmful content?
Such behaviour of Gen AI models is known as Hallucination wherein the model displays irrelevant outputs which are not related to inputs. Research continues to restrict models from hallucinating. Gen AI models are capable of hallucinations with high confidence, if not monitored correctly. As we talk about AI’s behaviour to completely mimic the human brain, there are challenges involved and the need for building trustworthy systems.
Ways to Tackle Hallucinations & Ground Them
Grounding term was introduced first by Microsoft. Grounding means providing with most accurate and factual answers that are related to the inputs provided.
Fine-tuning an AI model refers to using specific use case data over a pre-trained model. This helps the AI system to better come up with patterned and domain-constrained outputs. Fine-tuning can improve performance provided it is well-trained to learn patterns from the custom data. It is also known as a type of transfer learning as the weights of the pre-trained model get retrained over new data.
For example, a large language model such as GPT3 could be fine-tuned to predict sentiment analysis or an image model such as Vision Transformer could be fine-tuned to detect objects such as vehicles for autonomous driving.
There are some limitations to this process as it's highly time-consuming and expensive. Even after being trained, the model might not be able to perform well in preventing hallucinations.
Prompt engineering as the name suggests is a process of providing instructions to the generative AI model on what task needs to be performed. It suggests what content is to be generated, and further how it can be accomplished. This piece of information could be highly helpful in guiding the AI system into what exactly to look for. Different types of prompt engineering techniques are being explored. It can be in the form of text, image, speech anything. As an evident example, we can take up the most talked about these days is how OpenAI ChatGPT works. The better the prompts the more accurate are the results.
Similarly for image generation models, like StabilityAI’s stable diffusion has the option for negative prompts which could help provide some constraints.
Nevertheless, this method has its limitations in restraining the model from hallucination. Prompt engineering can’t always ensure the system is completely informed about the model’s domain data.
Retrieval-Augmented Generation as the name suggests consists of two neural networks retrieval-based and generative-based. The retrieval-based component uses a search algorithm to fetch relevant information from pre-existing data and knowledge based on the query or prompt provided. This information is then fed into the next layer i.e. the generative-based component, which could be RNNs, transformers or GPT models that generate content based on the retrieved information. The second layer is performing the summarization process from the inputs received from the first layer. This can be particularly useful in cases where the AI system has limited or incomplete information. RAG models had come up some years back and have been used in conversational bots, image captioning, language and other content generation.
It is hard to say if the AI system could be grounded and not hallucinate even with this process as it depends on pre-existing data that could have bias. Though one way round to overcome this could be human evaluation, about which we talk more in the next section.
We’re all aware of how reinforcement learning works with the hit-and-trial method. For every correct answer model is rewarded and for wrong answers it receives punishment. This way the model learns when and what actions to take. This is commonly how most games are designed.
In generative AI, reinforcement learning can be adequately useful for avoiding hallucinations. This can be done in two ways one is bot-based (adversarial networks) and another is human feedback or human-in-the-loop.
Adversarial networks are built of generator and discriminator models such as GANs(generative adversarial networks). The generative model is responsible for generating synthetic data until the discriminator model is unable to discriminate between the synthetic data from the real-world data. If properly trained, adversarial networks could be helpful to restrict models from hallucinations and ground the AI system from suggesting against the required outputs.
RLHF or reinforcement learning from human feedback is not new and has been used earlier. There are ways in which the system keeps generating multiple variations of the content and presenting them for human evaluation. Humans can continuously evaluate the variations based on factors such as coherence, relevance, and quality, and provide feedback back to the AI system. The AI system then uses this feedback to adjust its generation strategy and learns to generate new variations that are closer to the desired outcome. Though this method is expensive and time-consuming could prove to show better results. It could also have its challenges and how far the humans can outperform themselves is another story. For more information on this, check out Chip Huyen’s detailed blog on the same.
Above all these techniques can be clubbed and mixed-matched to get better results. Fine-tuning along with prompt engineering could prove to enhance results for certain use cases. Further usage of reinforcement learning with human feedback can be used to overcome certain edge cases.
It also depends on factors such as the dataset size, compute power(which usually is required a lot for such massive data-hungry models), the correlation between the input data and how the model can learn the pattern, etc. Training and building such systems can scale to unforeseen capabilities. This article is inspired and elaborated on my recent learnings and the below podcast I came across.


ST Spins Its First Linux-Powered Cortex-A SoC
STMicroelectronics introduced its first Cortex-A SoC and first Linux- and Android-driven processor. The STM32MP1 SoC intends to ease the transition for builders transferring from its STM32 microprocessor unit (MCU) household to extra complicated embedded programs. Growth boards based mostly on the SoC shall be out there in April.
Geared toward industrial, shopper, sensible residence, well being, and wellness purposes, the STM32MP1 options twin, 650MHz Cortex-A7 cores working a brand new “mainlined, open-sourced” OpenSTLinux distro with Yocto Mission and OpenEmbedded underpinnings. There’s additionally a 209MHz Cortex-M4 chip with an FPU, MPU, and DSP directions. The Cortex-M4 is supported by an enhanced model of ST’s STM32Dice growth instruments that assist the Cortex-A7 cores along with the M4 (see under).
Like most of NXP’s current embedded SoCs, together with the single- or -dual Cortex-A7 i.MX7 and its newer, Cortex-A53 i.MX8M and that i.MX8M Mini, the STM32MP1 is a hybrid Cortex-A/M design meant in ST’s phrases to “carry out quick processing and real-time duties on a single chip.” Hybrid Cortex-A7/M4 SoCs are additionally out there from Renesas, Marvell, and MediaTek, which has developed a custom-built MT3620 SoC because the hardware basis for Microsoft’s Azure Sphere IoT framework.
Because the market chief in Cortex-A MCUs, ST has made a bigger leap from its consolation zone than these different semiconductor distributors. NXP can be a number one MCU vendor, nevertheless it’s been crafting Linux-powered Cortex-A SoCs since lengthy earlier than it modified it named from Freescale. The SM32MP1 launch continues a development of MCU expertise corporations reaching out to the Linux group, comparable to Arm’s new Mbed Linux distro and Pelion IoT Platform, which orchestrates Cortex-M and Cortex-A units underneath a single IoT platform.
Contained in the STM32MP1
The STM32MP1 is provided with 32KB instruction and information caches, in addition to a 256KB L2 cache. ST additionally provides an elective Vivante 3D GPU with assist for OpenGL ES 2.zero and 24-bit parallel RGB shows at as much as WXGA (1280×800) at 60fps.
The SoC helps a 2-lane MIPI-DSI interface working at 1Gbps and gives native assist for Linux and utility frameworks comparable to Android Qt and Crank Software program’s Storyboard GUI. Whereas the GPU is fairly run-of-the-mill for Cortex-A7 SoCs it’s an enormous leap from the attitude of MCU builders making an attempt to deliver up trendy HMI shows.
Three SoC fashions can be found: one with 3D GPU, MIPI-DSI, and 2x CAN FD interfaces, in addition to one with 2x CAN FD solely and one with out the GPU and CAN I/O.
The STM32MP1 is touted for its rolling 10-year longevity assist and heterogeneous structure, which lets builders halt the Cortex-A7 and run solely on the Cortex-M4 to cut back energy consumption by 25 p.c. From this mode, “going to Standby additional cuts energy by 2.5k instances — whereas nonetheless supporting the resumption of Linux execution in 1 to three seconds, relying on the appliance,” says ST. The SoC features a PMIC and different energy circuitry comparable to buck and enhance converters.
For safety, the SoC supplies Arm TrustZone, cryptography, hash, safe boot, anti-tamper pins, and a real-time clock. RAM assist consists of 32/16-bit, 533MHz DDR3, DDR3L, LPDDR2, LPDDR3. Flash assist consists of SD, eMMC, NAND, and NOR.
Peripherals embody Cortex-A7 linked GbE, 3x USB 2.zero, I2C, and a number of UART and SPI hyperlinks. Analog I/O related to the Cortex-M4 embody 2x 16-bit ADCs, 2x 12-bit DACs, 29x timers, 3x watchdogs, LDOs, and as much as 176 GPIOs.
OpenSTLinux, STM32Dice, and starter kits
The brand new OpenSTLinux distribution “has already been reviewed and accepted by the “Linux group: Linux Basis, Yocto venture, and Linaro,” says ST. The Linux BSP consists of mainline kernel, drivers, boot chain, and Linaro’s OP-TEE (Trusted Execution Setting) safety stack. It additionally consists of Wayland/Weston, Gstreamer, and ALSA libraries.
Three Linux software program growth packages can be found: a fast Starter bundle with STM32CubeMP1 samples; a Dev bundle with a Yocto Mission SDK that permits you to add your individual Linux code; and an OpenEmbedded based mostly Distrib bundle that additionally enables you to create your individual OpenSTLinux-based Linux distro. ST has collaborated with Timesys on the Linux BSPs and with Witekio to port Android to STM32MP1.
STM32 builders can “simply discover their marks” by utilizing the acquainted STM32Dice toolset to manage each the Cortex-M4 and Cortex-A7 chips. The toolset consists of GCC-based STM32CubeProgrammer and STM32CubeMX IDEs, which “embody the DRAM interface tuning software for simple configuration of the DRAM sub-system,” says ST.
Lastly, ST is supporting its chip with a 4 growth boards: the entry stage STM32MP157A-DK1 and STM32MP157C-DK2 and the upper finish STM32MP157A-EV1 and STM32MP157C-EV1. All of the boards provide GPIO connectors for the Raspberry Pi and Arduino Uno V3.
The DK1/DK2 boards are geared up with 4GB DDR3L, in addition to USB Kind-C, USB Kind-A OTG, HDMI, and MIPI-DSI. You additionally get GbE and WiFi/Bluetooth, and a Four-inch, VGA capacitive contact panel, amongst different options.
The extra superior A-EV1 and C-EV1 boards assist as much as 8GB DDR3L, 32GB eMMCv5.zero. a microSD slot, and SPI and NAND flash. They supply a lot of the options of the DK boards, in addition to CAN, digital camera assist, SAI, SPDIF, digital mics, analog audio, and far more. In addition they provide 4x USB host ports and a micro-USB port. A 5.5-inch 720×1280 touchscreen is out there.


We utilize fast analytical systems for high speed monitoring of cellular secretion. We also use multi-color laser-induced fluorescence detection for achieving spectral resolution in cases where spatial resolution is limited.
Adams, A. G.; Bulusu, R. K. M.; Mukhitov, N.; Mendoza-Cortes, J. L.; Roper, M. G. "Online measurement of glucose consumption from HepG2 cells using an integrated bioreactor and enzymatic assay" Analytical Chemistry2019, 91, 5184-5190.PDF
Evans, K.; Wang, X.; Roper, M. G. "Chiral micellar electrokinetic chromatographic separation for determination of L- and D-primary amines released from murine islets of Langerhans" Analytical Methods, 2019, 11, 1276-1283.PDF
Bandak, B.; Yi, L.; Roper, M. G. “Microfluidic-enabled quantitative measurements of insulin release dynamics from single islets of Langerhans in response to 5-palmitic acid hydroxy stearic acid” Lab on a Chip2018, 18, 2873-2882. PDF
Wang, X.; Yi, L.; Roper, M. G. “Microfluidic device for the measurement of amino acid secretion dynamics from murine and human islets of Langerhans” Analytical Chemistry2016, 88, 3369-3375. PDF
Yi, L.; Wang, X.; Bethge, L.; Klussmann, S.; Roper, M. G. “Noncompetitive affinity assays of glucagon and amylin using mirror-image aptamers as affinity probes” Analyst2016, 141, 1939-1946. PDF
Measurement of Cellular Secretion
The fabrication of these devices can be tricky, especially when attempting to make extremely shallow channels. Follow this link to a protocol that we have found to have good success at making glass microfluidic chips. This protocol has been published and should be cited if used:
Baker, C. A.; Roper, M. G. “Continuous-flow, microchip electrophoresis fraction collector” Journal of Chromatography A 2010, 1217, 4743-4748.
Fabrication of Glass Microfluidic Devices
Microfluidic devices are ideal systems to automate various aspects of sample preparation. We have developed several devices that are being used to derivatize, stimulate, and collect from biological samples.
Filla, R. T.; Schrell, A. M.; Coulton, J. B.; Edwards, J. L.; Roper, M. G. “Frequency-modulated continuous flow analysis electrospray ionization mass spectrometry (FM-CFA-ESI-MS) for sample multiplexing” Analytical Chemistry2018, 90, 2414-2419. PDF
Mukhitov, N.; Spear, J. M.; Stagg, S. M.; Roper, M. G. “Interfacing microfluidics with negative stain transmission electron microscopy” Analytical Chemistry2016, 88, 629-634. PDF
Baker, C. A.; Roper, M. G. “Online coupling of digital microfluidic devices with mass spectrometry detection using an eductor with electrospray ionization” Analytical Chemistry 2012, 84, 2955-2960. PDF
Yu, Y.; Li, B.; Baker, C. A.; Zhang, X.; Roper, M. G. “Quantitative polymerase chain reaction using infrared heating on a microfluidic chip” Analytical Chemistry 2012, 84, 2825-2829. PDF
Islets of Langerhans Dynamics
We are developing microfluidic devices that can deliver reagents to islets of Langerhans in complex temporal patterns to investigate how islets respond to these dynamic stimulations.
Mukhitov, N.; Adablah, J. E.; Roper, M. G. "Gene expression patterns in synchronized islet populations" Islets2019,11, 21-32.PDF
Adablah, J. E.; Vinson, R.; Roper, M. G.; Bertram, R. "Synchronization of pancreatic islets by periodic or non-periodic muscarinic agonist pulse trains" PLoS One2019, 14, e0211832.PDF
McKenna, J. P.; Dhumpa, R.; Mukhitov, N.; Roper, M. G.; Bertram, R. “Glucose oscillations can activate an endogenous oscillator in pancreatic islets” PLoS Computational Biology2016, 12, e1005143. PDF
Dhumpa, R.; Truong, T. M.; Wang, X.; Roper, M. G. "Measurement of the entrainment window of islets of Langerhans by microfluidic delivery of a chirped glucose waveform" Integrative Biology2015, 7, 1061-1067. PDF
Yi, L.; Wang, X.; Dhumpa, R.; Schrell, A. M.; Mukhitov, N.; Roper, M. G. "Integrated perfusion and separation systems for entrainment of insulin secretion from islets of Langerhans" Lab on a Chip2015, 15, 823-832. PDF
At Florida state university


Susan is Professor of Technologies and Informatics at the Institute for Transport Studies, University of Leeds and a Fellow of The Alan Turing Institute. She leads a programme of research into large scale data analytics and the role of new forms of technology enabled data in developing sustainable transport policy.
A statistician by discipline, she researches within the Intelligent Transport Systems field. Recently this has concerned the use of new and emerging data forms, social media, mobility profiling and the role of web enabled technologies in the transport sector. Her current interests lie in understanding the wider impacts of ICT enabled mobility infrastructure (such as health, energy and carbon impacts), the governance of micro-level user-generated data, and understanding incentivisation as part of behaviourally orientated demand management.
Susan is a Co-Investigator for the ESRC Consumer Data Research Centre (CDRC) a member of the Leeds Institute for Data Analytics (LIDA) and the University of Leeds Centre for Integrated Energy Research (CIER) funded by a substantive tranche of University strategic funding


The unprecedented rise in remote work presents plenty of opportunities and challenges for organizations. While businesses being tied to physical locations no longer limits the talent pools from which they can draw, enabling effective communication and collaboration across dispersed teams is no simple task.
Fortunately, it is a challenge with no end of possible solutions – some of which work better than others. Generative AI is one of the newest technologies that can be leveraged to help manage remote teams – the same technology that powers the popular Chat GPT.
In this article, we’ll explore the role of Generative AI in managing remote teams and how this technology can be best utilized to bring dispersed teams together.
What is Generative AI?
Launched in late 2023, Chat GPT 3.5 quickly became a global sensation and one of the fastest-adopted technologies in history. Since then, other examples have come to market, including Google’s Bard and Claude from Anthropic.
These AIs leverage a variety of technologies to generate text, images, video, or audio in seconds based on user prompts. Anyone can use publicly available Generative AI tools to produce realistic content, including poems, code, scripts, musical pieces, art, videos, and marketing copy. This innovative software can automate content creation, personalize user experiences, and revolutionize virtual collaboration. It has the potential to revolutionize the future of work.
Generative AI will likely remain at the forefront of innovation for years. As we continue to blur the lines between man and machine, this technology holds exciting potential for businesses and individuals.
The role of Generative AI in managing remote teams
Generative AI is on the rise, and distributed teams can take full advantage of this innovative technology. Tools like ChatGPT and Bard can offer more than simple content generation, they can enhance communication, act as translators, and foster more creative problem-solving environments. Here’s a quick overview of how Generative AI can be used to manage remote teams:
- Real-time information hub: Generative AI tools can act like a dynamic, interactive knowledge base accessible to all team members worldwide. This eliminates the frustration of waiting for responses, ensures everyone has the same information and data, and facilitates rapid decision-making. AI can also act as a virtual support mentor for new employees, making the onboarding process more efficient.
- Improves communication: Generative AI can revolutionize communication workflows, helping remote workers get automated responses quickly without waiting for their team leaders to be online. Remote agents can instantly ask nuanced questions and receive relevant, human-like responses, boosting efficiency, productivity, and engagement.
- Encourages creative solutions: If AI is used correctly, it can unlock enhanced levels of creativity and collaboration among remote teams. Generative AI can facilitate discussions and trigger new perspectives, fostering more creative brainstorming sessions and a more open-minded problem-solving process. This leads to remote teams finding better solutions more quickly.
- Accommodates multilingual teams: Generative AI can facilitate communication and collaboration between multilingual teams with machine-learning translation capabilities. This improves company-wide efficiency, fosters diversity and inclusivity, and creates a more engaging workplace environment.
- Facilitates project management: Project management is often plagued by mundane administrative tasks. With generative AI, you can automate routine processes like task allocation, progress tracking, and deadline management to free up valuable time for team members to focus on strategic planning, problem-solving, and client engagement.
Generative AI is a powerful tool with the potential to revolutionize remote team operations. If utilized correctly, it can facilitate real-time information access, enhance communication and creativity, break down language barriers, and streamline project management.
Enhancing project management through AI
Generative AI is being integrated into existing software to turn simple project management tools into dynamic hubs of information and support.
Automation has become a key feature of modern project management. Thanks to Generative AI, algorithms can now seamlessly handle tasks that once consumed valuable time, allowing project managers to focus on more strategic aspects of running a business. This enables a more dynamic and flexible approach to managing complex tasks, and AI can even be used to generate creative solutions, suggest modifications, and navigate unexpected challenges.
Project managers can further utilize generative AI in the workplace to gather and analyze vast amounts of real-time data. This gives them greater insight into their projects, allowing them to anticipate potential obstacles, allocate support more effectively, and identify areas for improvement. This proactive approach helps mitigate challenges and optimize resources to ensure the project is delivered on time and within budget.
Simplifying documentation and report management
Organizing documentation and reports can be challenging and time-consuming for project managers, team leaders, and anyone else handling administration. Fortunately, Generative AI can help streamline these processes.
Menial tasks like drafting reports, preparing presentations, and organizing data-intensive documents can now be automated by AI, freeing up team members from the grind of paperwork and data entry. This increases efficiency and boosts engagement among remote employees.
Generative AI also eliminates human error during data entry and reporting. With impressive organizational and natural language processing (NLP) capabilities, AI tools can sort files and retrieve information quickly and efficiently. This avoids confusion and errors in fast-paced environments and allows everyone to find relevant answers easily.
Artificial intelligence can even offer personalized support and recommendations, tailoring their responses to your needs. This means you can ask AI to summarize complex reports, retrieve critical information quickly, and generate presentations based on your data.
Overcoming language barriers in global teams
Technological developments over the past century have made the world inter-connected like never before. While this has opened up the talent pool for many companies, language barriers can still impede effective collaboration.
However, with Generative AI, remote teams can enjoy seamless communication thanks to automated translation software and machine-learning capabilities. AI can act as a real-time translator and interpreter, helping everyone in your global workforce fully express themselves and understand one another.
Instead of relying on static translations or hesitant English communication, team members can now share ideas and offer suggestions in their native languages, leading to a more inclusive workplace that celebrates diversity and encourages collaboration.
Generative AI is a powerful tool that can improve the diversity and efficiency of your global communications, eliminating communication barriers. By reducing translation errors, these tools can even be used in important situations such as shareholder meetings, allowing every team member to express themselves fully and clearly.
Streamlining training and onboarding
Transitioning to a new role can be daunting, especially for remote employees who may lack the in-person guidance and support traditionally associated with onboarding. Fortunately, Generative AI can provide 24/7 support for training and onboarding remote team members.
Secure and interactive AI platforms can act as information hubs for new starters. Employees can receive real-time guidance, immediate answers to questions, and personalized feedback remotely without needing in-person training. This helps build confidence, amass knowledge, and improve engagement among new team members.
AI tools are also great for collating and disseminating information among remote employees. This allows new hires to access relevant data and learn from their peers in a hands-on manner, even in a remote environment, which accelerates their learning. In essence, Generative AI can act as a virtual mentor for new employees, enhancing the onboarding process through access to information and collaborative learning approaches.
A catalyst for creativity and innovation
In modern business, innovation is crucial to success. However, remote teams can experience challenges generating fresh ideas and overcoming creative roadblocks. This is where Generative AI can become a powerful catalyst for creativity.
For one, AI tools can enrich brainstorming sessions. Tedious meetings centered around a whiteboard are replaced with digital mind maps, virtual idea generators, and stimulating thought experiments. This pushes teams beyond conventional thinking and encourages them to explore more diverse and innovative solutions.
Generative AI tools can provide inspiration for remote workers by generating content ideas, helping to draw up plans, and offering suggestions based on the task at hand. These suggestions may prompt employees to think of concepts and connections they might have otherwise not found, helping to accelerate problem-solving processes with more innovative solutions.
Learn more about the future of work: remote vs hybrid, the impact of AI, and the necessity of upskilling in our podcast
The challenges of using Generative AI
While Generative AI holds immense promise for remote teams, its implementation comes with several challenges that need careful consideration:
- Technical infrastructure: Generative AI models can be computationally expensive to run, requiring significant resources. They also generally require extensive amounts of data for training, which can be time-consuming to collect, collate, tag, and structure so that the AI can use it. Seamless integration of Generative AI tools with existing workflow management systems and communication platforms is essential for smooth adoption and user experience.
- Data privacy and security: In addition to the data required for training, teams will feed lots of potentially sensitive company data to the AI in order to get responses from it. All of this data, along with the responses the AI produces, needs to be encrypted in transit and in storage. Robust security measures are needed to protect AI systems from cyberattacks and unauthorized access.
- Data quality and availability: The quality and availability of data are critical for training effective AI models. Access to diverse and high-quality data can be challenging, especially for smaller organizations or specific industries.
- Training and adaptation: Introducing any new technology can be met with resistance from employees who prefer established ways of working. Addressing concerns, providing adequate training, and highlighting the benefits are crucial for successful adoption. Remote teams may need training in using the technology, interpreting its outputs, and collaborating effectively with AI assistance.
- Ethics and bias: The use of Generative AI in remote work raises ethical concerns around data privacy, algorithm transparency, and the potential automation of jobs. Addressing these concerns openly and establishing ethical guidelines for AI use is essential. AI models can inherit biases and unfairness from the data they are trained on. This risk is amplified in remote settings, where diverse cultural and linguistic backgrounds are present. Mitigating bias and ensuring fair outcomes for all team members is critical.
- Quality control and oversight: Given Generative AI’s complex nature, ensuring human oversight and control over AI outputs, particularly in sensitive areas like decision-making, remains a technical challenge. It’s also essential to find the right balance between human-AI collaboration and interaction to ensure human judgment remains in the loop while leveraging AI’s efficiencies.
- Cost and ROI consideration: Implementing and maintaining Generative AI infrastructure can be expensive and require ongoing technical expertise. Finding the right balance between investment and value generation is essential. Due to high computational demands, smaller companies can face considerable challenges in acquiring and maintaining the necessary hardware and expertise.
A roadmap for implementing Generative AI
Embracing Generative AI in remote work settings requires creating responsible AI principles, bolstering data security and privacy, equipping employees with AI literacy, designing AI to be a human collaborator, and investing in continuous development to refine accuracy and efficiency. Here’s a brief roadmap to guide you through a successful implementation:
- Identify goals and use cases: Pinpoint the specific areas where Generative AI can add the most value. Is it streamlining communication and collaboration? Supercharging project management? Perhaps you envision AI crafting documents, facilitating onboarding, or bridging language barriers.
- Select tools and models: Research available Generative AI tools and models, meticulously evaluating their capabilities against your goals. Consider user-friendliness, seamless integration with existing platforms, robust security measures, and responsible data privacy practices.
- Pilot and refine: Introduce your AI model to specific tasks or teams, creating a controlled environment for learning and refinement. Gather valuable feedback from these early adopters and use it to fine-tune the system before rolling it out.
- Educate and train: Ensure your employees are equipped to interact effectively with Generative AI. Provide clear training on using and collaborating with these tools. Address concerns about job displacement head-on, emphasizing the human-AI partnership and the potential for mutual growth.
- Integrate with existing workflows: To ensure smooth adoption, prioritize seamless integration of Generative AI tools with your company’s communication, collaboration, and project management platforms. This minimizes disruption and maximizes user acceptance.
- Implement data protection: Data security and privacy are non-negotiables. Establish clear policies for data access and usage, building trust and preventing misuse of sensitive information. Robust security measures are essential to keep your implementation safe and secure.
- Adapt and adjust: Track key metrics and user feedback, be prepared to adapt, and fine-tune the system based on data and insights. Remember, an iterative approach is critical to long-term success.
- Build trust and transparency: Maintain open communication with your employees about Generative AI, addressing concerns and questions transparently. This fosters trust and acceptance of the new technology, ensuring everyone feels confident and prepared for the journey ahead.
- Ongoing development and refinement: AI is constantly evolving. Stay updated on the latest advancements and explore new opportunities to enhance its capabilities and applications within your organization. Continuous learning and development will ensure your Generative AI plan remains on track and reaches its full potential.
By following these steps and taking a cautious yet ambitious approach, you can effectively implement Generative AI in your remote workplace. This will empower your distributed teams to collaborate more seamlessly, work smarter, and achieve new heights of productivity and efficiency.
The future of Generative AI in remote workspaces
As the landscape of remote work continues to evolve, Generative AI will play an increasingly central role in the operations of distributed teams. Generative AI is here to stay thanks to its ability to streamline workflows, enhance project management, and facilitate global communication.
Despite its enormous benefits, however, Generative AI also comes with challenges. Data privacy, ethical AI employment, and potential algorithm biases will require thoughtful consideration to avoid the potential pitfalls of an overreliance on AI.
Navigating these challenges will define the effectiveness, inclusivity, and ethical landscape of remote workspaces for years to come. For now, at least, Generative AI and automation tools like Time Doctor can genuinely revolutionize the management of remote teams.
Carlo Borja is the Head of Content Marketing for Time Doctor, a productivity analytics software for distributed teams. He is a remote work advocate, a father and a coffee junkie.


Machine learning is a critical aspect of modern businesses and research. Using algorithms and neural networks, machine learning assists the computer system in improving their performance. They build models using simple data that helps in making decisions without needing to be programmed.
The first case of neural networks took place in 1943, when a mathematician Walter Pitts and a neurophysiologist Warren McCulloch wrote a paper on neurons and how they function. This was when they decided they would create a model of this with the help of an electrical circuit, thus the birth of the neural network.
Further research on the Turing test was done where computers had to convince humans that it is a human and not a computer. Decades later, Google’s DeepMind AI game AlphaGo could successfully defeat the world’s number one player in the Go games, an ancient board game that is complex and is said to have a more possible configuration for pieces than the atoms present in the universe.
Undoubtedly, AI and machine learning have progressed to greater heights one cannot fathom, this ideally is driven by computational power.
As the thrive gets greater in creating computer chips in a classical computing manner, you will find more bits that are nearing the smallest molecular size. Therefore, machine learning cannot rely on computational power anymore to create powerful models. The reason why machine learning now turns to compositional learning.
Defining compositional learning?
The idea behind compositional learning states that one single model cannot do all the tasks. For example, when deep neural networks were used for one single task like classifying an image into a cat or a dog or recognizing cancer, they performed extremely well. Sadly, it has been observed that the model could perform only one task at a time.
As technology in AI and applications become more complex, the single neural network will only get larger. Once this happens, it may further account for more complications with more neurons. Thus, the ability to grow might reach a dead end.
Now if you combine all these neural networks to perform certain segments of a complete task, as a whole the model tends to perform better even while taking up intricate tasks, maintaining a reasonable computing space.
If these tasks are broken down into fragments (several neural networks), each of these networks can specialize in a certain field. This relationship is almost similar to asking the prime minister to make decisions with or without any support from the secretaries of defense, health, labor, or any other departments.
Here’s a simple example,
A chatbot is developed to help upscale a restaurant’s business by engaging with customers or performing tasks like making a reservation or inquiring about the menu, or having general chitchat. The conversation can be easily broken into three sections – chitchat, information retrieval, and action are taken.
Well, instead of having one single model that takes in the customers’ reactions and sends an output – response, we can easily opt for a more distributed system.
Single model: current message history > general neural network (encoder – decoder) > response
Compositional model: current message history > task distributor neural network >
- Pleasantries network > response
- Info retrieval network > response
- Action network > response
With the help of a distributed model, you get two benefits:
- Faster runtime: though training distributed models can be a challenging process, they work even much faster while making predictions that require quick responses. This is possible because the distributed model can be thought of as splitting a singular model – thus, making the information pass through useful neurons that contain the current task instead of needing it to flow throughout the entire network.
- Higher accuracy: the tasks have been delegated to three separate models, each of which specializes in their field improving the performance of the model.
Encoder-decoder networks or GANs (generative models) are composed of multiple networks and at times can be said as compositional models. However, in the current context, they will still be considered as a singular model since compositional models expand making it more effective. The compositional model we’re talking about here is described as the ‘composition-composition model’.
One of the major reasons why compositional learning works perfectly well is perhaps because our brains are also compositional in nature. To be precise, compositional learning is much more difficult than standard modeling.
However, we’re yet to unveil what AI has in store for compositional learning.


We develop, manufacture and sell devices for creating and maintaining a microclimate in control and automation cabinets, as well as in various electrical equipment.
The idea of creating the SILART brand arose as a result of the currency crisis in Russia in 2014-2015. It was the unstable economic situation and the “spirit of import substitution” that prompted us to implement plans to organize the production of Russian electrical products under the SILART brand.
In 2016, we developed, organized production and launched the first batch of mechanical bimetal thermostats of the TBS series. In 2017-2018, we launched the production of convection heaters of the SNK series, fan heaters of the SNV series. As part of joint OEM projects and based on technologies from leading German manufacturers, we have supplied a wide range of air conditioners of the MALTA series. In 2018-2020, we significantly expanded the range of products we develop and manufacture by introducing new lines of convection heaters and roof fans.
The successful experience of our team in the field of complex provision of enterprises and organizations with electrical components from the world's leading manufacturers allows us to confidently consider ourselves professionals in the electrical equipment market, who "have studied well what the consumer likes, but even better studied what the consumer does not like."
We have an ambitious mission: we contribute to the revival of the Russian industry, build a world-class electrical company, creating technologies that are safe for humans and the environment.