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With how easy they can make our day-to-day lives, it is easy to get swept up by the impressive achievements of apps like <keyword id="19918826" type="General" weightage="20" keywordseo="ChatGPT" source="keywords">ChatGPT</keyword>, Midjourney, and <keyword id="20055508" type="General" weightage="20" keywordseo="DALL-E" source="keywords">DALL-E</keyword>. These <keyword id="21195046" type="General" weightage="20" keywordseo="AI-applications" source="keywords">AI applications</keyword>, powered by <keyword id="19897804" type="General" weightage="20" keywordseo="large-language-models" source="keywords">large language models</keyword> (LLMs), can generate text, create images, and hold conversations that mimic human interactions. But according to a recent study by Apple Computer Company researchers, the intelligence displayed by these AI systems is far from genuine. <h2>The illusion of intelligence</h2>The Apple research team, led by a trio of AI experts, conducted a series of tests on various popular LLMs. They found that while these models appear intelligent on the surface, their responses are often unreliable when asked to perform tasks requiring true <keyword id="21233087" type="General" weightage="20" keywordseo="logical-reasoning" source="keywords">logical reasoning</keyword>. This discrepancy, they argue, reveals that LLMs don’t actually “understand” the questions they’re answering. Instead, they simply recognise familiar patterns and respond based on statistical probabilities, rather than meaningful comprehension. To illustrate this point, the researchers used a simple analogy: imagine a child asking their parent how many apples are in a bag. The child also mentions that some of the apples are too small to eat. Both the child and the parent understand that the size of the apples has no bearing on their quantity. However, when <keyword id="19924380" type="General" weightage="20" keywordseo="AI-models" source="keywords">AI models</keyword> are presented with similarly structured questions — questions with information that can be considered unnecessary (like the size of the apples) — they frequently become confused, offering nonsensical or incorrect answers. <div type="Article" hostid="261" msid="114046455" title="Love ChatGPT and Alexa? This year’s Physics Nobel Prize has been awarded to two scientists who made these AI technologies possible" mstype="2" mssubtype="0"></div>In their study, the researchers asked the AI models hundreds of questions, including some with non-pertinent details. They found that even minor changes in phrasing or context were enough to throw off the models. These AIs, despite their advanced programming, struggled to filter out irrelevant information, suggesting that they lack the capacity for genuine logical reasoning. <h2>How do modern AI apps work?</h2>Understanding why AI models like ChatGPT struggle with logic and nuance requires a closer look at how these systems function. At their core, LLMs are based on complex machine learning algorithms that analyse vast amounts of text data to learn language patterns. Once trained, these models use probabilities to generate responses based on what they’ve seen in their training data. However, they don’t “understand” language the way humans do. When an LLM receives a prompt, it breaks down the text into tokens and predicts the next word in a sequence based on statistical likelihood. This approach allows the AI to generate coherent, contextually relevant responses but doesn’t equip it to handle questions that require deeper, contextual reasoning. In essence, AI models are mimicking language without truly grasping the meaning behind it. To elaborate on the earlier example, if asked, “How many apples are in a bag if three are too small to eat?” a human would immediately recognise that the size of the apples has no impact on the total count. An AI, on the other hand, may get tripped up by the additional detail and offer a convoluted response because it cannot separate what’s important from what’s not. Apple’s researchers suggest that this limitation is a fundamental flaw in how AI models are designed. While the technology has advanced rapidly, the underlying mechanics remain rooted in <keyword id="20034261" type="General" weightage="20" keywordseo="pattern-recognition" source="keywords">pattern recognition</keyword> rather than true comprehension. As a result, today’s AI applications are better suited for tasks that involve straightforward pattern matching, like language translation or text prediction, but struggle when asked to exhibit <keyword id="23580940" type="General" weightage="20" keywordseo="human-like-intelligence" source="keywords">human-like intelligence</keyword>. <h2>Overestimating AI capabilities can have risks</h2>Because these modern AI models can produce remarkably human-like text, many have a tendency to attribute greater intelligence and understanding to them than they actually possess. This misconception can lead to unintended consequences, such as relying on AI for critical decision-making in areas where accuracy and logical reasoning are paramount — such as in business decisions as more and more companies adopt AI. <div type="Article" hostid="261" msid="114284609" title="Even high school interns are able to use common AI tools to bypass security and launch bots on X, Facebook and other social media: report" mstype="2" mssubtype="0"></div>The researchers also caution that the “illusion” of intelligence can make it difficult for users to recognise when an AI is providing incorrect information. In their tests, Apple’s team noted that LLMs frequently gave answers that sounded correct on the surface but fell apart upon closer inspection. These findings highlight the importance of scrutinising AI-generated content and using these tools as supplements to human expertise rather than replacements for it. As AI continues to evolve, researchers will need to explore new approaches that go beyond pattern recognition. Achieving genuine AI intelligence will likely require breakthroughs in fields like cognitive computing and neuromorphic engineering — fields that aim to create systems capable of human-like understanding and reasoning. For now, the takeaway from Apple’s research is clear: despite their impressive capabilities, today’s AI apps are tools, not thinkers. As Apple researchers pointed out, these models are designed to simulate understanding but lack the cognitive depth to handle complex reasoning. Recognising these limitations is crucial as we integrate AI into more aspects of our lives. The findings of this research have been published in a preprint server and can be accessed <a href="https://dx.doi.org/10.48550/arxiv.2410.05229">here</a>.

Apple engineers assert the AI apps of today are essentially still just fakes that lack 'true' intelligence

<ul><li>Researchers have created an <keyword id="1341083" type="General" weightage="20" keywordseo="electronic-skin" source="Orion">electronic skin</keyword> that can feel pain.</li><li>The working of artificial skin is based on synaptic transistors that eliminate the response time.</li><li>This <keyword id="15509" type="General" weightage="20" keywordseo="research" source="Orion">research</keyword> may open new doors for the development of touch-sensitive <keyword id="16719" type="General" weightage="20" keywordseo="robots" source="Orion">robots</keyword>.</li></ul>A team of researchers led by Professor Ravinder Dahiya, an Indian-origin engineer in the UK, has developed an electronic skin capable of sensing pain. According to the team, it could help them to create a next-gen series of robots with human-like sensitivity. Professor Ravinder Dahiya comes from the University of Glasgow’s James Watt School of Engineering. He says believes the research takes us a step forward in creating a large-scale neuromorphic printed e-skin capable of responding to stimuli. The news was shared by the University of Glasgow's Twitter handle. <div type="twitter" handle="UofGlasgow" handlename="University of Glasgow" image="http://pbs.twimg.com/profile_images/1278260281446215690/ut-tJgml_normal.jpg" favoritecount="19" retweetcount="10" status="A new type of electronic skin capable of feeling 'pain' could help create a new generation of smart robots and pros… https://t.co/4NWBAgKq61" createdat="1654156800000" id="1532270797418508293"></div>The study was published in the paper titled 'Printed Synaptic Transistors based Electronic Skin for Robots to Feel and Learn' in the journal <a href="https://www.science.org/doi/10.1126/scirobotics.abl7286">Science Robotics</a>. According to the researcher, the development of the electronic skin is the latest breakthrough in stretchable, flexible printing technology from Glasgow's Bendable Electronics and Sensing Technologies (BEST) group. Earlier, scientists have been working on building artificial skin with touch sensitivity capabilities. Some of the explored methods include the use of pressure sensors across the electronic skin surface allowing it to detect when comes in contact. Received data from the sensors is sent to a computer for processing. The large volume of data takes time to process which leads to a delay in response reducing the effectiveness of artificial skin in the real world. The new form of electronic skin from Glasgow University draws inspiration from a different human peripheral nervous system that interprets signals from the skin eliminating the response time. As soon the e-skin receives an input, the system process the data at the point of contact. This makes sure only the vital information is being sent to the brain. To enhance the capabilities of electronic skin, Professor Ravinder Dahiya and his team printed a grid of 168 synaptic transistors (computing system that performs signal processing) onto the surface of artificial skin. They connected the synaptic transistors with the skin sensors that are present over the human-shaped robot hand. When these sensors are touched they register changes in their electrical resistance system. Light touch creates light resistance and harder touch creates larger resistance. Potential applications of electronic skin Researchers from the University of Utah have developed a prosthetic arm- <a href="https://unews.utah.edu/star-wars-inspired-arm/">LUKE arm</a> that mimics a sense of human touch. The LUKE arm uses an array of microelectrodes implanted in the prosthetic arm that are connected to a computer. It allows better communication between the prosthetic arm and brain. Though the arm is limited to nineteen touch sensors it can only move in limited directions. Incorporating electronic skin into the LUKE arm can open new doors for researchers allowing them to develop next-gen prosthetic limbs. SEE ALSO: <a href="https://www.businessinsider.in/tech/enterprise/news/how-do-data-analytics-leaders-ensure-data-quality-and-success-of-their-data-projects/articleshow/92029802.cms">Attention: High-quality Data is the New Oil</a> <a href="https://www.businessinsider.in/tech/enterprise/news/how-to-avoid-burnout-of-compliance-team/articleshow/92029499.cms">Is your compliance team on the verge of a burnout?</a>

A team of researchers led by an Indian-origin engineer developed an electronic skin that feels pain

For most of us, <keyword id="1829745" type="UNKNOWN" weightage="100" keywordseo="thinking-machines">thinking machines</keyword> happen to be sci-fi stuff, but for those working in the <keyword id="107950" type="TECHNOLOGY" weightage="100" keywordseo="artificial-intelligence">artificial intelligence</keyword> (<keyword id="6355999" type="PERSON" weightage="100" keywordseo="AI">AI</keyword>) space, that is just a ground reality. AI is slowly gaining traction among <keyword id="100363" type="UNKNOWN" weightage="100" keywordseo="Indian-entrepreneurs">Indian entrepreneurs</keyword> and a handful of companies are already working on one product or the other, targeting specific segments. But here is one brand new <keyword id="6404679" type="UNKNOWN" weightage="100" keywordseo="start-up">start-up</keyword> which is hoping to open up this field and make it super easy for <keyword id="6357020" type="PERSON" weightage="100" keywordseo="developers">developers</keyword> to <keyword id="1973991" type="UNKNOWN" weightage="100" keywordseo="Incorporate-AI-components">incorporate AI components</keyword> in their apps as per their requirements. Chennai-based <keyword id="1973988" type="UNKNOWN" weightage="100" keywordseo="Mad-Street-Den">Mad Street Den</keyword> (interestingly, ‘<keyword id="6707506" type="ORGANIZATION" weightage="100" keywordseo="Mad">MAD</keyword>’ stands for <keyword id="1973989" type="UNKNOWN" weightage="100" keywordseo="Mind-Abled-Devices">Mind Abled Devices</keyword>) will be offering easy-to-access <keyword id="1973997" type="UNKNOWN" weightage="100" keywordseo="AI-solutions">AI solutions</keyword> via a Cloud-based platform, thus delivering intelligence to the world, on a broad scale. The <keyword id="1974000" type="UNKNOWN" weightage="100" keywordseo="IA-stack">IA stack</keyword> comprises <keyword id="1974001" type="UNKNOWN" weightage="100" keywordseo="Sensory-processing-layers">sensory processing layers</keyword>, as well as <keyword id="1974004" type="UNKNOWN" weightage="100" keywordseo="Contextual-binding-layers">contextual binding layers</keyword>, and aims to deliver three categories of <keyword id="1974005" type="UNKNOWN" weightage="100" keywordseo="Consumer-experiences">consumer experiences</keyword>. 
 But that does not mean lack of more intriguing products on the part of the start-up. Mad Street Den is all about intelligent innovation and the company is working intensely on a series of products using vision, speech and touch-based processing (among others), aiming to cater to verticals like <keyword id="6357332" type="UNKNOWN" weightage="100" keywordseo="gaming">gaming</keyword> and <keyword id="4578" type="COMPANY" weightage="100" keywordseo="entertainment">entertainment</keyword>, <keyword id="6354750" type="CITY" weightage="100" keywordseo="health">health</keyword> and <keyword id="15482" type="COMPANY" weightage="100" keywordseo="lifestyle">lifestyle</keyword>, and <keyword id="6355300" type="CITY" weightage="100" keywordseo="security">security</keyword> and <keyword id="6359249" type="COMPANY" weightage="100" keywordseo="analytics">analytics</keyword>. Here is a snapshot that captures the start-up’s vision and viability. 
 
Who runs Mad Street Den (www.madstreetden.com): A core team of four including <keyword id="1718234" type="POSITION" weightage="80" keywordseo="Co-Founder-CEO">co-founder &amp; CEO</keyword> <keyword id="1973987" type="UNKNOWN" weightage="100" keywordseo="Anand-Chandrasekaran">Anand Chandrasekaran</keyword>. Anand holds a degree in Electrical Engineering from IIT-Madras and a PhD in Neuroscience from <keyword id="503459" type="ORGANIZATION" weightage="80" keywordseo="Baylor-College-of-Medicine">Baylor College of Medicine</keyword>. During his Post Doctoral stint at <keyword id="33081" type="ORGANIZATION" weightage="80" keywordseo="Stanford">Stanford</keyword>, he specialised in <keyword id="1974014" type="UNKNOWN" weightage="100" keywordseo="Neuromorphic-Engineering">Neuromorphic Engineering</keyword>, building <keyword id="1974017" type="UNKNOWN" weightage="100" keywordseo="Custom-silicon-chips">custom silicon chips</keyword> that emulate various regions of the brain. At that time, Anand was part of the <keyword id="10875" type="CITY" weightage="80" keywordseo="IBM">IBM</keyword> team and working on <keyword id="265122" type="ORGANIZATION" weightage="100" keywordseo="DARPA">DARPA</keyword>’s <keyword id="1974019" type="COMPANY" weightage="100" keywordseo="SyNAPSE">SyNAPSE</keyword> project (for the curious, let’s tell you this project focuses on building all sorts of <keyword id="1974020" type="UNKNOWN" weightage="100" keywordseo="Smart-hardware">smart hardware</keyword> that emulate higher intelligence). 
 Prior to starting his own venture, Anand joined a Palo Alto-based start-up called Evolved Machines, Inc., as a research scientist and developed various neural algorithms that could connect to real-world sensory perceptions like vision and smell. “As part of Evolved Machines, I was involved in another DARPA project called <keyword id="1974022" type="UNKNOWN" weightage="100" keywordseo="Neovision2">Neovision2</keyword>,” he recalls. 
 There are three others in the core team – another <keyword id="170488" type="POSITION" weightage="100" keywordseo="co-founder">co-founder</keyword> who has some very useful experience in designing products that use the <keyword id="294572" type="TECHNOLOGY" weightage="100" keywordseo="machine-learning">machine learning</keyword> approach; an experienced and distinguished professional with a similar background to Anand’s and another expert who builds the kind of network infrastructure the company requires. Although development work started in March this year (a month after Anand returned from <keyword id="6357722" type="COMPANY" weightage="100" keywordseo="silicon-valley">Silicon Valley</keyword>), the start-up was officially launched in August 2013 and has recently shifted its headquarters from Bangalore to Chennai. 
 
What inspired the venture: During his stint at Evolved Machines, Anand decided that he would work on AI projects of his own. So he worked as a <keyword id="17182" type="POSITION" weightage="60" keywordseo="Consultant">consultant</keyword> with <keyword id="1973986" type="UNKNOWN" weightage="100" keywordseo="Zort-Technologies">Zort Technologies</keyword> for a couple of years while trying to figure out what to do next. 
 “Although building great AI is a good thing, we did realise that there were very few solutions in the market that would help create great <keyword id="1974006" type="UNKNOWN" weightage="100" keywordseo="Consumer-experience">consumer experience</keyword>,” says Anand. “That’s because there is no unified platform that allows easy and cheap <keyword id="1974027" type="UNKNOWN" weightage="100" keywordseo="Access-to-AI-elements">access to AI elements</keyword> – no suitable infrastructure for handling the requirements of the applications using those elements. So we decided to create such a platform. That way, we can build a whole new ecosystem for everyone who wants to create new experiences enhanced by AI.” 
 
What’s the pitch: Making machines more useful by making them a bit more human: Fun, intelligent and relevant. They will sense as babies, observe and play as youngsters, learn and change with you as they grow older. In brief, the start-up aims to provide AI support across three categories of experiences – fun and play, usefulness &amp; convenience, and helpful &amp; revealing. “Someday in the near future, we hope to bring everyday objects to life, not just the devices we use today,” the site notes. 
 
How it works: Mad Street Den is in the business of delivering artificial intelligence to the world – in varied formats and across industries. The primary product is a Cloud-based <keyword id="1974029" type="UNKNOWN" weightage="100" keywordseo="AI-platform">AI platform</keyword> that developers can leverage to incorporate <keyword id="1973990" type="UNKNOWN" weightage="100" keywordseo="AI-components">AI components</keyword> into their applications. This ‘MAD’ (Mind Abled Devices) stack currently focuses on <keyword id="1974030" type="UNKNOWN" weightage="100" keywordseo="Vision-based-applications">vision-based applications</keyword> and delivers elements such as <keyword id="1974031" type="UNKNOWN" weightage="100" keywordseo="Facial-gesture-analysis">facial gesture analysis</keyword>, <keyword id="406688" type="UNKNOWN" weightage="100" keywordseo="face-recognition">face recognition</keyword>, <keyword id="1974032" type="UNKNOWN" weightage="100" keywordseo="Gaze-tracking">gaze tracking</keyword> and so on. However, the start-up will expand and diversify its offerings as the platform grows. As of now, the target sectors include apps, <keyword id="34609" type="UNKNOWN" weightage="100" keywordseo="services-industry">services industry</keyword>, gaming and analytics (imagine test-marketing a product and getting insightful feedback through AI apps that can decode facial gestures of potential customers and correctly surmise their reaction to the product in question). But in the next few years, Mad Street Den may also develop low-cost <keyword id="1974033" type="UNKNOWN" weightage="100" keywordseo="Custom-hardware">custom hardware</keyword> to support its existing stack; create <keyword id="1974034" type="UNKNOWN" weightage="100" keywordseo="High-end-cognitive-hardware">high-end cognitive hardware</keyword> for big enterprises/software companies and finally make foray into <keyword id="62549" type="UNKNOWN" weightage="100" keywordseo="Robotics">robotics</keyword>. 
 
“All these are in the pipeline and we intend to create more apps and new products by leveraging our own AI platform,” explains Anand. “That will further create new and wonderful experiences enhanced by our AI solutions and speak volumes about our quality.” 
 
Claim to fame: Novelty of concept and the tremendous potential are the key factors at this point. Very few <keyword id="770060" type="UNKNOWN" weightage="100" keywordseo="Indian-start-ups">Indian start-ups</keyword> are currently working in the <keyword id="1974055" type="UNKNOWN" weightage="100" keywordseo="AI-space">AI space</keyword> and it could be the next big thing when Web 3.0 – widely assumed to be a combination of artificial intelligence and the semantic Web – finally arrives. 
 
Show me the money: Till date, the start-up has been bootstrapped and has not started earning revenues. As of now, the <keyword id="232730" type="POSITION" weightage="100" keywordseo="business-model">business model</keyword> is essentially B2B, but Anand has high hopes about B2C earnings as the company is also building its own products. “We are a very early-stage company, completely self-funded and in the process of building our scalable infrastructure and our first B2C product (a game) by early next year. In fact, we will be demoing our prototype at MobileSparks 2013 in Bangalore tomorrow (December 14), he says. 
 “As we have many potential <keyword id="598465" type="UNKNOWN" weightage="100" keywordseo="revenue-streams">revenue streams</keyword>, it is a little difficult to gauge the market size at present,” he observes. “But it spans everyone – from the <keyword id="1974050" type="UNKNOWN" weightage="100" keywordseo="College-dorm-developer">college dorm developer</keyword> to <keyword id="1974051" type="UNKNOWN" weightage="100" keywordseo="Small-product-companies">small product companies</keyword>, to service providers and <keyword id="163194" type="COMPANY" weightage="100" keywordseo="OEMs">OEMs</keyword> who want to enhance their offerings. Even <keyword id="1974052" type="UNKNOWN" weightage="100" keywordseo="Large-corporations">large corporations</keyword> might want their own custom stacks and proprietary <keyword id="1974026" type="UNKNOWN" weightage="100" keywordseo="AI-elements">AI elements</keyword>,” he adds. 
 Although bootstrapped, Mad Street Den is not looking to raise funds just now. “We intend to work on our product pipeline first and come up with 1-3 more products by mid-2014,” says Anand. “Once we have the process in play and are getting considerable traction at consumer and enterprise levels, approaching investors will make sense. Also, we have gone past the incubation stage now. Neither are we limited to a single platform or product. Therefore, we will be looking at a very large angel round or maybe a small <keyword id="919124" type="UNKNOWN" weightage="100" keywordseo="Series-A">Series A</keyword> within a year, depending on our requirements. Given our <keyword id="1974053" type="UNKNOWN" weightage="100" keywordseo="RD-requirements">R&amp;D requirements</keyword>, we would like to expand our operations to the US as well.” 
 
Biggest challenge: Competition is always there as several companies are working in the AI space – both in India and abroad. But the field is vast and almost completely untapped; so there is space for everyone. “Currently, there’s no delivery model like our Cloud platform, especially in the vision space. So that gives us some competitive edge and we are building the rest of the USP,” concludes Anand.

This
Start-up Offers Artificial Intelligence Solutions To Enhance Experiences, Aims
To Tackle Analytics Challenge

Researchers at the Indian Institute of Science (<keyword id="41780" type="General" weightage="20" keywordseo="iisc" source="Orion">IISc</keyword>) have developed a brain-inspired analog computing platform capable of storing and processing <keyword id="6355729" type="General" weightage="20" keywordseo="data" source="Orion">data</keyword> in an astonishing 16,500 conductance states within a molecular film. Published on September 11 in the journal Nature, this breakthrough represents a huge step forward over traditional digital computers in which data storage and processing are limited to just two states, the IISc s<keyword id="6355999" type="General" weightage="20" keywordseo="ai" source="Orion">ai</keyword>d. In a press release, the institute said that such a platform could potentially bring complex AI tasks, like training Large Language Models (LLMs), to personal devices like laptops and smartphones, thus taking us closer to democratising the development of AI tools. These developments are currently restricted to resource-heavy data centres, due to a lack of energy-efficient hardware. With silicon electronics nearing saturation, designing brain-inspired accelerators that can work alongside silicon chips to deliver faster, more efficient AI is also becoming crucial. "Neuromorphic computing has had its fair share of unsolved challenges for over a decade," explained <keyword id="23122611" type="General" weightage="20" keywordseo="sreetosh-goswami" source="keywords">Sreetosh Goswami</keyword>, Assistant Professor at the Centre for Nano Science and Engineering (CeNSE), IISc, who led the research team. "With this discovery, we have almost nailed the perfect system - a rare feat," he said. According to IISc, the fundamental operation underlying most AI algorithms is quite basic - matrix multiplication, a concept taught in high school maths. But in digital computers, these calculations hog a lot of energy. The platform developed by the IISc team drastically cuts down both the time and energy involved, making these calculations a lot faster and easier. The molecular system at the heart of the platform was designed by <keyword id="23122609" type="General" weightage="20" keywordseo="sreebrata-goswami" source="keywords">Sreebrata Goswami</keyword>, Visiting Professor at CeNSE. As molecules and ions wiggle and move within a material film, they create countless unique memory states, many of which have been inaccessible so far. Most digital devices are only able to access two states (high and low conductance), without being able to tap into the infinite number of intermediate states possible. By using precisely timed voltage pulses, the IISc team found a way to effectively trace a much larger number of molecular movements, and map each of these to a distinct electrical signal, forming an extensive "molecular diary" of different states. "This project brought together the precision of electrical engineering with the creativity of chemistry, letting us control molecular kinetics very precisely inside an electronic circuit powered by nanosecond voltage pulses," explained Goswami. "Tapping into these tiny molecular changes allowed the team to create a highly precise and efficient neuromorphic accelerator, which can store and process data within the same location, similar to the human brain. Such accelerators can be seamlessly integrated with silicon circuits to boost their performance and energy efficiency," the IISc said. IISc researchers said the key challenge that the team faced was characterising the various conductance states, which proved impossible using existing equipment. The team designed a custom circuit board that could measure voltages as tiny as a millionth of a volt, to pinpoint these individual states with unprecedented accuracy. It also turned this scientific discovery into a technological feat. The team was able to recreate NASA's iconic "Pillars of Creation" image from the James Webb Space Telescope data - originally created by a <keyword id="6537818" type="General" weightage="20" keywordseo="supercomputer" source="Orion">supercomputer</keyword> - using just a tabletop computer. They were also able to do this at a fraction of the time and energy that traditional computers would need. Using their AI accelerator, the team recreated NASA's iconic "Pillars of Creation" image from the James Webb Space Telescope data on a tabletop computer - achieving this in a fraction of the time and energy required by traditional systems, it said. The team includes several students and research fellows at IISc. They also collaborated with Stanley Williams, Professor at Texas A&amp;M University and Damien Thompson, Professor at the University of Limerick, it added. The researchers believe that this breakthrough could be one of India's biggest leaps in AI hardware, putting the country on the map of global technology innovation. Navakanta Bhat, Professor at CeNSE and an expert in silicon electronics led the circuit and system design in this project. "What stands out is how we have transformed complex physics and chemistry understanding into groundbreaking technology for AI hardware," Goswami explained. "In the context of the India Semiconductor Mission, this development could be a game-changer, revolutionising industrial, consumer and strategic applications. The national importance of such research cannot be overstated," he said. With support from the Ministry of Electronics and Information Technology, the IISc team is now focused on developing a fully indigenous integrated neuromorphic chip. "This is a completely home-grown effort, from materials to circuits and systems," he emphasised. "We are well on our way to translating this technology into a system-on-a-chip," he added.

IISc scientists develop brain-inspired analog computing platform capable of storing, processing data

<ul class="summary-list"><li>The supercomputer, named DeepSouth, is being developed by Western Sydney University in Australia.</li><li>When it goes online next year, it will be capable of 228 trillion synaptic operations per second.</li></ul>Our brains are remarkably energy efficient.Using just 20 watts of power, the human brain is capable of processing the equivalent of an exaflop — or a billion-billion mathematical operations per second.Now, researchers in Australia are building what will be the world's first supercomputer that can simulate networks at this scale.The supercomputer, known as DeepSouth, is being developed by Western Sydney University.When it goes online next year, it will be capable of 228 trillion synaptic operations per second, which rivals the estimated rate of operations in the human brain.The hope is to better understand how brains can use such little power to process huge amounts of information.If researchers can work this out, they could someday create a <a target="_blank" href="https://www.businessinsider.com/elon-musk-cyborgs-machines-ai-technology-2023-6">cyborg</a> brain vastly more powerful than our own. The work could also revolutionize our understanding of how our brains work."Progress in our understanding of how brains compute using neurons is hampered by our inability to simulate <a target="_blank" href="https://www.businessinsider.com/ai-generated-images-persons-thoughts-brain-scan-study-2023-3">brain-like networks</a> at scale," said André van Schaik, a director at Western Sydney University's International Centre for Neuromorphic Systems."Simulating spiking neural networks on standard computers using Graphics Processing Units and multicore Central Processing Units is just too slow and power intensive," he added. "Our system will change that."Ralph Etienne-Cummings at Johns Hopkins University, Baltimore, who is not involved in the work, told <a target="_blank" href="https://www.newscientist.com/article/2408015-supercomputer-that-simulates-entire-human-brain-will-switch-on-in-2024/">New Scientist</a> that DeepSouth will be a game changer for the study of neuroscience."If you are trying to understand the brain this will be the hardware to do it on," he said.Etienne-Cummings said that there will be two main types of researchers who will be interested in the technology — those studying neuroscience, and those who want to prototype new engineering solutions in the AI space.DeepSouth is just one of many research projects aiming to create a machine that will rival the human brain.Other researchers are trying to tackle the same problem by creating <a target="_blank" rel="noopener noreferrer" class="c-link" href="https://www.ft.com/content/9f51a30d-eedc-446a-8a5c-d2997c670c65">"biological computers"</a> powered by actual brain cells.

The world's first human brain-scale supercomputer will go live next year

<ul class="summary-list"><li>Naveen Rao is the VP of generative AI at Databricks and cofounded the LLM training platform MosaicML.</li><li>Rao says copyright infringement could prevent companies from successfully monetizing AI.</li></ul>Generative artificial intelligence has a monetization problem, says Naveen Rao.Rao, who oversees generative-AI strategy for Databricks after it bought his startup MosaicML for <a target="_blank" href="https://www.databricks.com/company/newsroom/press-releases/databricks-signs-definitive-agreement-acquire-mosaicml-leading-generative-ai-platform">$1.3 billion</a>, likens it to the issue that crushed Napster, the 2000s-era music-sharing platform.Napster, wildly popular at first, fundamentally changed the way people consumed and shared music until it was sued over copyright infringement so many times that it went bankrupt three years after its launch. It has been bought and sold so many times since then that it's now a shadow of its former self. Meanwhile, Apple launched iTunes and became the dominant force in online music streaming.Rao sees a similar scenario playing out with generative AI, which writes or creates art but only after ingesting vast amounts of data to train its models. OpenAI's introduction of ChatGPT last year has sparked a frenzy of AI-model training — and a lot of concern that those models are using copyrighted material, training themselves to imitate it, or even using actual bits and pieces of it, leaving individuals open to intellectual-property infringement and companies vulnerable to lawsuits.Such lawsuits are already beginning. Just last month, a group of 17 popular authors, including Jodi Picoult and the "Game of Thrones" creator George R.R. Martin, <a target="_blank" href="https://www.businessinsider.com/game-of-thrones-author-sues-chatgpt-openai-copyright-infringement-lawsuit-2023-9">sued OpenAI in federal court alleging "systematic theft on a mass scale"</a> over concerns their work was being used to train its models. For Rao, this lawsuit is reminiscent of some of the first lawsuits against Napster, such as the one Metallica filed in 2000."That needs to be respected," Rao said of copyrighted materials. "And we need tools to do that."Rao has spent his career building those tools for this moment. A verification engineer by training with a doctorate in neuroscience, Rao was researching neuromorphic machines — computers inspired by the human brain — at Qualcomm. He sold his first company, the deep-learning startup Nervana, to Intel in 2016 for <a target="_blank" href="https://www.businessinsider.com/intel-buys-nervana-systems-for-at-least-350-million-2016-8">more than $350 million</a>.With MosaicML, Rao built a platform that offered companies foundational models to turn into their own large language models and train them with their own data in a secure environment.Rao's thesis is that if companies have a way to use their own data safely for model training on a transparent, open-source platform, they'll be free from worrying about legal challenges and able to successfully monetize their AI-based services.Data sources aside, Rao sees another key business reason for companies to build their own LLMs: differentiation. MosaicML's platform provides a user-friendly infrastructure for companies to build their own models, something Rao says competitors such as OpenAI don't necessarily offer."We build tools that enable companies to differentiate their AI from everyone else's and leverage their data uniquely," Rao told Insider.Rao thinks of Mosaic's technology as a kind of democratization of generative AI, and he's brought that ethos to Databricks, which in April launched its own open-source-trained LLM named Dolly that companies can use to help train their own models as well. The more people building generative-AI technology, the better, Rao says."What's interesting about technology is there's always some element of it that can be used in nefarious ways," Rao said. "But the way to stop that is by having more people armed with the same tools and being able to use them for good."

Generative AI needs tools to avoid copyright infringement, Databricks' Naveen Rao says &mdash; or more companies could meet Napster's fate

A scientist from <keyword id="75850" type="General" weightage="20" keywordseo="indian-institute-of-science" source="Orion">Indian institute of Science</keyword> (IISc), Bengaluru, has worked on applications of emerging materials that can help computers mimic functions that the brain can perform rapidly. "While several synaptic device proposals are available in literature, none helps bridge the gap between biological neural networks and their artificial counterpart. Our work has shown that developing systems that can mimic brain-like function is achievable. It can help computers perform cognitive functions, identify people from a crowd, distinguish smell as well as learn and take decisions," IISc's Professor Mayank Shrivastava, who is also a recipient of Swarna Jayanti Fellowship 2020-21, said. Since their inception, advanced computing systems have been using von <keyword id="13076084" type="General" weightage="20" keywordseo="neumann-architecture" source="Orion">Neumann architecture</keyword>, which uses physically separated processing and memory blocks. While it has been the most cost-effective approach to date, physical separation of memory from the processing blocks has become the rate limiter for pushing the computational capabilities of advanced nano-electronic systems. Besides, von Neumann's architecture fails in real-time processing of information that the <keyword id="67889" type="General" weightage="20" keywordseo="human-brain" source="Orion">human brain</keyword> can process in a fraction of a second. Keeping these gaps in mind, an alternative architecture, inspired by the organisation of neurons (processing unit) and synapses (memory) in the human brain that emulates brain-like computing behaviour, have been explored significantly in the last decade. A significant amount of research to understand the fundamental mechanisms of the brain and explorations of various novel memory architectures is now giving the engineering community confidence that developing systems that can mimic brain-like function is an achievable goal for the coming decades. A key element of such an architecture is a memory device called the artificial synapse, which, however, must work on biological / synaptic principles. Professor Srivastava is exploring materials such as Gallium Nitride (GaN), atomically thin two-dimensional materials like Graphene, and Transition metal dichalcogenides (TMDCs), for various electronics, power devices, electro-optic, Thz, memory, and quantum applications. Using Graphene and 2D-TMDCc, he is extending capabilities of memory devices to work on biological / synaptic principles and bridge the gap between biological neural networks and their artificial counterpart. Currently, along with his research group, he is developing few-atom thick neuromorphic circuits, GaN-based ultra-high-power devices with high reliability, and devices / circuits for operation at THz frequencies. Professor Shrivastava's work has resulted in over 150 peer-reviewed international publications and around 50 patents, and he has set a GaN manufacturing startup named AGNIT Semiconductors. Most of these patents are either licensed by semiconductor companies or are in use in their products. He is also one of the co-founders of a Pvt. Ltd. Company. As part of this fellowship, his group plans to work on novel devices emulating brain-like (computational) behaviour. This should eventually help developing few-atom thick neuromorphic circuits, which will be capable of solving several complex problems which a human brain can solve in a fraction of seconds, but a conventional computer can't in real-time. "We are still too far from realizing a brain-like computational system. (But) the present work will give a pathway to bridge the critical gaps," said Professor Shrivastava.

IISc scientist develops material that can help computers mimic brain-like functions

To encourage research work at Bengaluru-based <keyword id="75850" type="ORGANIZATION" weightage="80" keywordseo="Indian-Institute-of-Science">Indian Institute of Science</keyword> (IISc), <keyword id="6354512" type="CITY" weightage="80" keywordseo="Infosys">Infosys</keyword> co-founder Senapathy Gopalakrishnan, popularly known as <keyword id="6530028" type="PERSON" weightage="80" keywordseo="Kris-Gopalakrishnan">Kris Gopalakrishnan</keyword> and his wife Sudha have set up three <keyword id="608640" type="ORGANIZATION" weightage="80" keywordseo="Research-Chairs">research chairs</keyword> in the country’s top institution, according to an ET report. Gopalakrishnan, 60, hopes the chairs can bring distinguished researchers in the areas of computational neuroscience, <keyword id="294572" type="TECHNOLOGY" weightage="60" keywordseo="machine-learning">machine learning</keyword>, data <keyword id="41551" type="ENTERTAINMENT" weightage="80" keywordseo="science">science</keyword> and neuromorphic engineering to the IISc to enable collaborative research. The first of the three chairs — K Vaidyanathan Distinguished Chair named after Sudha's father — was launched on Monday. Each chair will be set up with a corpus of Rs 10 crore funded by <keyword id="2038697" type="UNKNOWN" weightage="80" keywordseo="Pratiksha-Trust">Pratiksha Trust</keyword>. It is a charitable organization Kris founded with his wife. 

	Moreover, the Pratiksha Trust has also helped to set up three similar chairs at the Indian Institute of Technology-Madras, where Kris studied. 
 

	"I hope the launching of these distinguished chair positions will help push the frontiers in these areas. It would be excellent if the collaborations lead to highly creative, new computing architectures and algorithms inspired by the functioning of the brain. These three chairs along with three more at IIT Madras will create critical mass in the area of neuromorphic computing and machine learning in India," Kris told the financial daily.
 

	The K Vaidyanathan Chair at the IISc will be occupied by Shihab Shamma, a professor at the Institute of Systems Research of the University of Maryland. 
 

	"I am sure these distinguished chair positions will add a new dimension to research collaboration in key strategic areas between IISc researchers and star contributors in these areas anywhere in the world," IISc Director Anurag Kumar told ET. 
 At <keyword id="54070" type="ORGANIZATION" weightage="80" keywordseo="IIT-Madras">IIT-Madras</keyword>, two of the three chairs — Prof Mahabala Chair and N R Narayana Murthy Chair — on computational brain research have also been launched. 

	Both chairs are occupied by US-based scientists of Indian origin. In early 2014, the Infosys cofounder announced a Rs 225-crore grant over a period of 10 years to develop a Centre for Brain Research (CBR) at the IISc. 
 

	This philanthropic act is widely-considered one of the biggest contributions to pure science in the country, and one of the single largest donations by an individual to the IISc. 

	 
	(Image: Reuters)

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