Mediaboss Marketing

ALISO VIEJO, Calif.--(BUSINESS WIRE)--BrainChip Holdings Ltd (ASX: BRN), (OTCQX: BRCHF), a leading provider of ultra-low power high performance artificial intelligence technology, today announced that Rob Telson, VP Worldwide Sales and Marketing, will be a featured presenter at the AI Hardware Summit at 1:25 p.m. PDT, September 13th. This hybrid event will be in-person and virtual with the BrainChip session streamed live. The company will also have an in-person demo booth at the Computer History Museum in Mountain View, California on September 14-15.

Telson will present Intelligent AI Everywhere, which will address how attendees can easily apply efficient AI in edge devices for many applications by implementing Akida IP into a system on a chip (SOC) or as standalone silicon. BrainChips Akida neural processor unit brings intelligent AI to the Edge everywhere, leveraging advanced neuromorphic computing as the engine to solve critical problems of privacy, security, latency and low power requirements with key features, such as one-shot learning and computing on the device with no dependency on the cloud. The presentation will also feature real-life examples of on-chip and on-device functionality. Demonstrations of Akidas abilities will also be on display at the BrainChip booth throughout the summit.

Sensors at the edge require real-time computation and managing both ultra-low power and latency requirements with traditional machine learning is extremely difficult when it comes to empowering smart intelligent edge devices, said Telson. I look forward to sharing with those attending AI Hardware Summit both in-person and virtually how BrainChip is delivering on next-generation demands by achieving efficient, effective and easy AI functionality everywhere.

BrainChips Akida brings artificial intelligence to the edge in a way that existing technologies are not capable. The solution is high-performance, small, ultra-low power and enables a wide array of edge capabilities. The Akida (NSoC) and intellectual property, can be used in applications including Smart Home, Smart Health, Smart City and Smart Transportation. These applications include but are not limited to home automation and remote controls, industrial IoT, robotics, security cameras, sensors, unmanned aircraft, autonomous vehicles, medical instruments, object detection, sound detection, odor and taste detection, gesture control and cybersecurity.

AI Hardware Summits mission is to help those who are accelerating AI workloads in the cloud and at the edge. With a theme of systems-level AI acceleration, this years event is lifting the hood on how to make AI fast, efficient and affordable. The three-day hybrid event features a virtual component September 13-15 with in-person attendance September 14 and 15. Additional information about AI Hardware Summit is available at https://aihardwaresummit.com

About BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF)

BrainChip is a global technology company that is producing a groundbreaking neuromorphic processor that brings artificial intelligence to the edge in a way that is beyond the capabilities of other products. The chip is high performance, small, ultra-low power and enables a wide array of edge capabilities that include on-chip training, learning and inference. The event-based neural network processor is inspired by the spiking nature of the human brain and is implemented in an industry standard digital process. By mimicking brain processing BrainChip has pioneered a processing architecture, called Akida, which is both scalable and flexible to address the requirements in edge devices. At the edge, sensor inputs are analyzed at the point of acquisition rather than through transmission via the cloud to a data center. Akida is designed to provide a complete ultra-low power and fast AI Edge Network for vision, audio, olfactory and smart transducer applications. The reduction in system latency provides faster response and a more power efficient system that can reduce the large carbon footprint of data centers.

Additional information is available at https://www.brainchipinc.com

Follow BrainChip on Twitter: https://www.twitter.com/BrainChip_inc Follow BrainChip on LinkedIn: https://www.linkedin.com/company/7792006

See the original post:
BrainChip Demonstrates that Intelligent AI is Everywhere at AI Hardware Summit 2021 - Business Wire

Before innovating any product or solution, it is essential to understand the pain points or challenges of the audience. Conversations with people in the audience help towards that goal, and the Samsung SDS team actively seeks them out to learn from them. Often, the challenge becomes clearer only when one understands the practical daily life of the users.

Patrick Bangert is the Vice President of Artificial Intelligence at Samsung SDS, America. He mentions that once, he was asked to construct a damage forecasting model for a certain kind of pump to lower maintenance costs. At that time, he lacked experience and was asked to do a three-day internship with the maintenance crew to find out. They took him along, and he performed the work with them; all the while experiencing what they go through. In the end, he had a clear picture of where the problem lay and then could solve it much more easily.

Patrick started his career as an Assistant Professor of mathematics at a new university that had only existed one year before he started working there. There was no curriculum set up for the courses that he was supposed to teach. So he started by putting together a coherent end-to-end program and then explaining it to a large audience of undergraduates. It was a difficult lesson that forced him to explain things in simple terms. The dual skill of making a complex subject accessible in simple terms and breaking it down into digestible pieces are crucial to leadership and inevitable sales and marketing function as a leader.

His challenges did not end there. Patrick explains how at the beginning of his journey, he did not know where to begin and lacked a clear vision of where he wanted to end up.

With hindsight, I recognize that having a vision of where you want to be in your own life at an older age is a boon because it acts as a guiding principle in decision-making.

Patrick recalls several people guiding him and offering him suggestions to do this and not do that. He followed the advice most of the time because he respected them but did not necessarily understand why, except for short-term tactical reasons. Naturally, this led him to make a few costly mistakes, the worst being choosing the wrong business partner in founding his startup company. He did not know how to perform proper due diligence and did not do it. His partner was unable to fulfill his responsibilities, creating a profoundly serious problem for the business. Subsequently, he was forced to change his role and fulfill both leadership functions at the same time. For him, this was an existential challenge, but he learned a great deal.

Currently, under his leadership, Samsung SDS is leveraging disruptive technologies to give the company a competitive edge in the market. These technologies make it easier and faster to innovate and enable several innovations themselves. Recently, Patrick has been working on using AI to diagnose diseases based on medical images. He says that creating these models is very effortful, particularly annotating the images to generate the dataset; on which to train the models. Using AI to help the annotators, the company could lower the labor by over 80%, making it significantly faster and cheaper to make models. The company is calling this technology AI2 since it is using AI to create more AI.

If we categorize human history by its most influential tool into stone, copper, bronze, and iron ages, then the current time has sometimes been called the information age. While the information itself is an asset, it is only really the conclusion of an analysis that adds value. So, currently, we live in the analytics age with a ubiquitous impact on further innovation, he said.

According to Patrick, there have been many changes to human society in recent centuries. The current AI-driven transition is the so-called fourth industrial revolution. There is some fear attached to the inherent change, as in any transition can make some jobs less needful, but other new job functions usually take their place. There is less and less need, at a grand scale, for manual labor. In the industry overall, the transition towards using AI is still in its early days. AI is mature only in the retail industry and in some isolated applications elsewhere. There is a long way to go to adopt the analytics we have. Those who have developed the analytics must work on making them better. The primary directions are AI ethics, explainability, and the challenge of small data.

As a large IT services company, Samsung SDS is the chief player in this transition and the importance of analytics grows both internally and externally. The company will keep developing new tools and models to serve the needs of the global business community. At present, it is actively focusing on the challenge of small data: the fact that often the dataset has a limited size and cannot be increased readily, so AI must make do. A host of new algorithms are needed to make good quality models once the standard quality improvement mechanismget more databecomes impossible.

As a Ph.D. mathematician, Patrick started to work in academia as a Professor in Germany and proceeded to found a startup in the field of machine learning as applied to the oil and gas and chemicals sector. During this time, he led projects all over the world to do predictive maintenance and process optimization for almost all the major oil companies. Having grown that company to maturity, he was hired as the VP for AI at Samsung SDS, the IT company of Samsung Group.

At Samsung SDS, he leads three teams. AI Engineering makes software tools for data scientists, primarily in speeding up the process through distributed training and AutoML. AI Sciences does the model building within Samsung Group and for outside customers. The third team does the sales and marketing for the other two teams. Besides the companys regular work in natural language processing, computer vision, and time-series modeling, it has recently shifted its focus to medical imaging.

Read more here:
Patrick Bangert- Leading the Artificial Intelligence Industry with Disruptive Innovation - Analytics Insight

AI research lab DeepMind has created the most comprehensive map of human proteins to date using artificial intelligence. The company, a subsidiary of Google-parent Alphabet, is releasing the data for free, with some scientists comparing the potential impact of the work to that of the Human Genome Project, an international effort to map every human gene.

Proteins are long, complex molecules that perform numerous tasks in the body, from building tissue to fighting disease. Their purpose is dictated by their structure, which folds like origami into complex and irregular shapes. Understanding how a protein folds helps explain its function, which in turn helps scientists with a range of tasks from pursuing fundamental research on how the body works, to designing new medicines and treatments.

Previously, determining the structure of a protein relied on expensive and time-consuming experiments. But last year DeepMind showed it can produce accurate predictions of a proteins structure using AI software called AlphaFold. Now, the company is releasing hundreds of thousands of predictions made by the program to the public.

I see this as the culmination of the entire 10-year-plus lifetime of DeepMind, company CEO and co-founder Demis Hassabis told The Verge. From the beginning, this is what we set out to do: to make breakthroughs in AI, test that on games like Go and Atari, [and] apply that to real-world problems, to see if we can accelerate scientific breakthroughs and use those to benefit humanity.

There are currently around 180,000 protein structures available in the public domain, each produced by experimental methods and accessible through the Protein Data Bank. DeepMind is releasing predictions for the structure of some 350,000 proteins across 20 different organisms, including animals like mice and fruit flies, and bacteria like E. coli. (There is some overlap between DeepMinds data and pre-existing protein structures, but exactly how much is difficult to quantify because of the nature of the models.) Most significantly, the release includes predictions for 98 percent of all human proteins, around 20,000 different structures, which are collectively known as the human proteome. It isnt the first public dataset of human proteins, but it is the most comprehensive and accurate.

If they want, scientists can download the entire human proteome for themselves, says AlphaFolds technical lead John Jumper. There is a HumanProteome.zip effectively, I think its about 50 gigabytes in size, Jumper tells The Verge. You can put it on a flash drive if you want, though it wouldnt do you much good without a computer for analysis!

After launching this first tranche of data, DeepMind plans to keep adding to the store of proteins, which will be maintained by Europes flagship life sciences lab, the European Molecular Biology Laboratory (EMBL). By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be transformative for our understanding of how life works, according to Edith Heard, director general of the EMBL.

The data will be free in perpetuity for both scientific and commercial researchers, says Hassabis. Anyone can use it for anything, the DeepMind CEO noted at a press briefing. They just need to credit the people involved in the citation.

Understanding a proteins structure is useful for scientists across a range of fields. The information can help design new medicines, synthesize novel enzymes that break down waste materials, and create crops that are resistant to viruses or extreme weather. Already, DeepMinds protein predictions are being used for medical research, including studying the workings of SARS-CoV-2, the virus that causes COVID-19.

New data will speed these efforts, but scientists note it will still take a lot of time to turn this information into real-world results. I dont think its going to be something that changes the way patients are treated within the year, but it will definitely have a huge impact for the scientific community, Marcelo C. Sousa, a professor at the University of Colorados biochemistry department, told The Verge.

Scientists will have to get used to having such information at their fingertips, says DeepMind senior research scientist Kathryn Tunyasuvunakool. As a biologist, I can confirm we have no playbook for looking at even 20,000 structures, so this [amount of data] is hugely unexpected, Tunyasuvunakool told The Verge. To be analyzing hundreds of thousands of structures its crazy.

Notably, though, DeepMinds software produces predictions of protein structures rather than experimentally determined models, which means that in some cases further work will be needed to verify the structure. DeepMind says it spent a lot of time building accuracy metrics into its AlphaFold software, which ranks how confident it is for each prediction.

Predictions of protein structures are still hugely useful, though. Determining a proteins structure through experimental methods is expensive, time-consuming, and relies on a lot of trial and error. That means even a low-confidence prediction can save scientists years of work by pointing them in the right direction for research.

Helen Walden, a professor of structural biology at the University of Glasgow, tells The Verge that DeepMinds data will significantly ease research bottlenecks, but that the laborious, resource-draining work of doing the biochemistry and biological evaluation of, for example, drug functions will remain.

Sousa, who has previously used data from AlphaFold in his work, says for scientists the impact will be felt immediately. In our collaboration we had with DeepMind, we had a dataset with a protein sample wed had for 10 years, and wed never got to the point of developing a model that fit, he says. DeepMind agreed to provide us with a structure, and they were able to solve the problem in 15 minutes after wed been sitting on it for 10 years.

Proteins are constructed from chains of amino acids, which come in 20 different varieties in the human body. As any individual protein can be comprised of hundreds of individual amino acids, each of which can fold and twist in different directions, it means a molecules final structure has an incredibly large number of possible configurations. One estimate is that the typical protein can be folded in 10^300 ways thats a 1 followed by 300 zeroes.

Because proteins are too small to examine with microscopes, scientists have had to indirectly determine their structure using expensive and complicated methods like nuclear magnetic resonance and X-ray crystallography. The idea of determining the structure of a protein simply by reading a list of its constituent amino acids has been long theorized but difficult to achieve, leading many to describe it as a grand challenge of biology.

In recent years, though, computational methods particularly those using artificial intelligence have suggested such analysis is possible. With these techniques, AI systems are trained on datasets of known protein structures and use this information to create their own predictions.

Many groups have been working on this problem for years, but DeepMinds deep bench of AI talent and access to computing resources allowed it to accelerate progress dramatically. Last year, the company competed in an international protein-folding competition known as CASP and blew away the competition. Its results were so accurate that computational biologist John Moult, one of CASPs co-founders, said that in some sense the problem [of protein folding] is solved.

DeepMinds AlphaFold program has been upgraded since last years CASP competition and is now 16 times faster. We can fold an average protein in a matter of minutes, most cases seconds, says Hassabis. The company also released the underlying code for AlphaFold last week as open-source, allowing others to build on its work in the future.

Liam McGuffin, a professor at Reading University who developed some of the UKs leading protein-folding software, praised the technical brilliance of AlphaFold, but also noted that the programs success relied on decades of prior research and public data. DeepMind has vast resources to keep this database up to date and they are better placed to do this than any single academic group, McGuffin told The Verge. I think academics would have got there in the end, but it would have been slower because were not as well resourced.

Many scientists The Verge spoke to noted the generosity of DeepMind in releasing this data for free. After all, the lab is owned by Google-parent Alphabet, which has been pouring huge amounts of resources into commercial healthcare projects. DeepMind itself loses a lot of money each year, and there have been numerous reports of tensions between the company and its parent firm over issues like research autonomy and commercial viability.

Hassabis, though, tells The Verge that the company always planned to make this information freely available, and that doing so is a fulfillment of DeepMinds founding ethos. He stresses that DeepMinds work is used in lots of places at Google almost anything you use, theres some of our technology thats part of that under the hood but that the companys primary goal has always been fundamental research.

The agreement when we got acquired is that we are here primarily to advance the state of AGI and AI technologies and then use that to accelerate scientific breakthroughs, says Hassabis. [Alphabet] has plenty of divisions focused on making money, he adds, noting that DeepMinds focus on research brings all sorts of benefits, in terms of prestige and goodwill for the scientific community. Theres many ways value can be attained.

Hassabis predicts that AlphaFold is a sign of things to come a project that shows the huge potential of artificial intelligence to handle messy problems like human biology.

I think were at a really exciting moment, he says. In the next decade, we, and others in the AI field, are hoping to produce amazing breakthroughs that will genuinely accelerate solutions to the really big problems we have here on Earth.

View original post here:
DeepMind creates transformative map of human proteins drawn by artificial intelligence - The Verge

In order to fulfill the rising food demand, great emphasis is being given to efficient farming through automation in the field and need-based resource management in farm operations to improve crop productivity. Though the scientific advances help us in understanding the crop, soil, weather, and in what conditions it can grow better, some aspects of agriculture can become more efficient by monitoring and predictive analysis for understanding the accurate status of crops, input requirements, and possible yield output. Artificial Intelligence (AI) and remote sensing can help us in this area. AI and advanced satellite imagery, along with weather forecasts provide a unique dataset that allows prediction of harvest, tracking the presence and spread of pests. This improves crop productivity, address food production issues without degrading environment, enhance farm incomes and can help tackle climate change effectively.

Written by Dr. Shivendra Bajaj, Executive Director, Federation of Seed Industry of India and Alliance for Agri Innovation

Dr Shivendra Bajaj

At present, there are several challenges that the agriculture sector is facing. Increase in production cost, lack of required water for irrigation, higher labor cost, fall in farm remuneration, frequent incidences of droughts and floods due to climate change are some of the major issues. To make it worse, the ongoing Covid-19 pandemic has disrupted the food production system and supply chains significantly.

India has seen rapid adoption of technological innovations in the agriculture sector in the recent past. Also, internet services are penetrating the rural areas at a fast pace. This sounds encouraging for the propagation of Artificial Intelligence-based tools for better farm management. One such example is AI tool being used to regulate water flow intelligently and prevent unrequired dampness in soil and thus rules out the possibility of pests that thrive under excessive moisture.

Precision choicesAI enables farmers to carry out farming activities with great precision as they can make informed choices on deciding the right crops and the right process to cultivate for better returns. Higher yields, healthier crops, effective pest control, soil monitoring are some of the major benefits. Artificial intelligence in agriculture involves sensors, robots, and drones to collect different kinds of information and perform tasks such as weeding, irrigation, spraying pesticides, and fertilizers without human intervention. The greenhouse emissions are decreased by 20 percent thanks to new ways of farming AI brings in. This is a significant contribution to the efforts being taken to cut carbon emissions.

At present, a major obstacle farmers face is a lack of timely and accurate data. Remote sensing, which gives valuable insights into various agronomic parameters, and AI facilitate farmers to micromanage standing crops by taking into account the weather conditions, market dynamics. These technological interventions capture green data from multiple sources, analyze it and turn into valuable insights to help stakeholder use fewer resources, manage farming activities accurately and efficiently.

The adoption of AI has so far been driven primarily from a commercial perspective thus we need to focus on how to benefit the agriculture sector at a large scale. NITI Aayog has sought to embrace AI to reduce our dependence on resource-intensive farm practices in order to boost agriculture value chain. There has been substantial rise of agricultural tech start-ups in the past few years. Yet, the adoption of AI has remained limited. NITI Aayog has narrowed down on some problem areas such as difficulty in access to data, high cost and low availability of computing infrastructure, lack of collaborative approach and public awareness.

Agriculture as a backboneThere is no doubt that agriculture is the backbone of India and supports the livelihood of over 50 percent of Indias population. Besides ensuring food security, the demand for healthy, chemical-free food from consumers, which is produced in a sustainable manner, all require the digital transformation in the agriculture sector. What we need to do is to ensure the availability of affordable hardware such as sensors and other communication devices, farm machinery, and equipment and software. Similarly, agriculture research institutes, universities must extend their support, build expertise and create awareness for the adoption of AI and remote sensing in agriculture. One such University in Andhra Pradesh, Acharya NG Ranga Agriculture University (ANGRAU) is using drones in the agriculture sector for various agri related activities. The university is now urging farmers to use drone technology to minimize the input cost on agriculture workers apart from effective practices of seed sowing and spraying pesticides. More colleges and universities should follow this suit.

We will have to start with building AI-enabling infrastructure that has region-specific, crop-specific databases. It will be followed up by capacity building and training of farmers on digital technologies including AI and remote sensing. These technologies may not be affordable to individual farmers so in such cases intermediaries such as FPOs or start-ups can address the problem. Using technology for efficient resources usage, we can achieve higher yields.

See the original post here:
India needs widespread adoption of Artificial Intelligence to improve crop productivity - hortidaily.com

A wide range of technology solutions purport to be "driven by AI," or artificial intelligence. But are they really? Not everything labeled AIis truly artificial intelligence. The technology, in reality, has not advanced nearly far enough to actually be "intelligent."

"AI is often a sensationalized topic," said Neil Morelli, chief industrial and organizational psychologist for Codility, an assessment platform designed to identify the best tech talent. "That makes it easy to swing from one extreme reaction to another," he said.

"On the one hand, fear of AI's misuse, 'uncontrollability,' and 'black box' characteristics. And on the other hand, a gleeful, over-hyped optimism and adoption based on overpromising or misunderstanding AI's capabilities and limitations." Both can lead to negative outcomes, he said.

Much of the confusion that exists over what AI is, or isn't, is driven by the overly broad use of the term, fueled to a large degree by popular entertainment, the media and misinformation.

[Want to learn more about the future of work? Join us at theSHRM Annual Conference & Expo 2021, taking place Sept. 9-12 in Las Vegas and virtually.]

What Is AI, Really?

"Much of what is labeled as 'artificial intelligence'today is not,"said Peter Scott, the founding director of Next Wave Institute, a technology training and coaching firm. "This mislabeling is so common we call it 'AI-washing.' "

The boundaries have often shifted when it comes to AI, he said. "AI has been described as 'what we can't do yet,' because as soon as we learn how to do it, we stop calling it AI."

The ultimate goal of AI, Scott said, "is to create a machine that thinks like a human, and many people feel that anything short of that doesn't deserve the name." That's one extreme.

On the other hand, most of those in the field "will say that if it uses machine learning, especially if it uses deep learning, then it is AI," he said. Officially, "AI is a superset of machine learning, which leaves enough wiggle room for legions of advertisers to ply their trade, because the difference between the two is not well-defined."

Jeff Kiske, director of engineering, machine learning at Ripcord, agrees. Most of what is called AI today could better be referred to as "machine learning," he said. This, he added, is how he prefers to refer to "cutting-edge, data-driven technology." The term machine learning, noted Kiske, "implies that the computer has learned to model a phenomenon based on data. When companies tout their products as 'driven by machine learning,' I would expect a significantly higher level of sophistication."

Joshua A. Gerlick, a Fowler Fellow at Case Western Reserve University in Cleveland, said that AI "is an incredibly broad field of study that encompasses many technologies." At the risk of oversimplification, he said, "a common theme that differentiates a 'true' from a 'misleading' AI system is whether it learns from patterns and features in the data that it is analyzing."

This is the promise of many use-cases in HR for machine learning that actually don't rise to the level of true artificial intelligence.

Implications for HR

For example, Gerlick said: "Imagine a human resources department acquiring software that is 'powered by AI' to match newly hired employees with an experienced mentor within the organization. The software is programmed to find common keywords in both the profiles of the mentees and potential mentors, and a selection is obtained based upon the highest mutual match." While an algorithm is certainly facilitating the matching process within the software, Gerlick said, "it is absolutely not an AI-powered algorithm. This algorithm is simply replicating a process that any human could accomplish, and although it is fast, it does not make the matchmaking process more effective."

A truly AI-powered software platform, he said, would require some initial datalike profiles of previous mentee-mentor pairs and whether the outcomes were successful. It would then learn the factors that led to a successful pairing. "In fact, the software would be so sensitive that it might only be applicable to identifying successful mentee-mentor pairs at this one specific organization," Gerlick said. "In a roundabout way, it has 'learned' how to understand the unique culture of the organization and the archetypes of individuals who work within it. A human resources executive should find that the AI-powered software platform improves its effectiveness over timeand hopefully exceeds the success of its human counterparts, leaving them the time to undertake more complex initiatives."

Christen da Costa, founder of Gadgetreview.com, saidhe thinks the term "AI" is thrown around far too readily. "Most automation tools, for example, are not what I would call AI," he noted. "They take in information fed to them by the user and look for cases that match it. Over time they learn the user's preferences and become better, but that's algorithmic learning. While it can be an aspect of AI, it does not an AI make."

Does it matter? It can. When HR professionals are considering adopting new technology, it's important to not be confusedor swayedby lofty tech terms that tend to be thrown around far too frequently.

It's also important to not be overly enamored of, or potentially misled by, the lure of "artificial intelligence."

"Thoughtful readers and observers of AI in HR would be wise to remember that AI systems help perform manual, repetitious and laborious tasks in HR," Codility'sMorelli said. "However, the range and scope of these tasks are probably narrower than some vendors and providers lead people to believe."

There is no AI system that understands, perceives, learns, pattern-matches or adapts on its own, he said. "Instead, it needs human-labeled and curated data as a starting point. For this reason, users and evaluators should apply more scrutiny to the training data used to teach AI systems," he said, "especially the data's origin, development and characteristics."

"When skeptical over whether a technology is truly 'powered by AI,' consider asking a few simple questions," Gerlick suggested:

If the answers to those questions are yes, he said, "then artificial intelligence might be lending a helping hand."

Lin Grensing-Pophal is a freelance writer in Chippewa Falls, Wis.

Read more here:
Not All AI Is Really AI: What You Need to Know - SHRM