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This Robot Has Taken Its First Baby Step With Zero Computer Simulation

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This Robot Has Taken Its First Baby Step With Zero Computer Simulation

AI technique, reinforcement learning to train the robot dog to walk from scratch in the real world

The University of California, Berkeley researchers have built a unique robot: one that taught itself how to walk. The robot dog is waving its legs in the air like an exasperated beetle. After 10 minutes of struggling, it manages to roll over to its front. The research is remarkable as this robot, a four-legged device reminiscent of a mechanical puppy, learned to walk by itself, without being shown any simulations to instruct it beforehand. Furthermore, inaccuracies in the world models these robots use are very damaging to their performance, and constructing reliable world models takes a lot of time and data.

Reinforcement learning to train the robot dog:

Researchers used an AI technique called reinforcement learning, which trains algorithms by rewarding them for desired actions, to train the robot dog to walk from scratch in the real world. The robot dog is taking its first clumsy steps, like a newborn calf. But after one hour, the robot is strutting around the lab with confidence. 

The common approach in training robots is to use computer simulations to let them grasp the basics of whatever they are doing before making them attempt the same tasks in the real world. Traditionally, robots are trained in a computer simulator before they attempt to do anything in the real world. Teaching robots through trial and error is a difficult problem, made even harder by the long training times such teaching requires.

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With reinforcement learning, engineers need to specify in their code which behaviors are good and are thus rewarded, and which behaviors are undesirable. Using this approach, the team successfully trained three other robots to perform different tasks, such as picking up balls and moving them between trays. A new generation of reinforcement-learning algorithms could pick up on the real-working workings, super quickly.

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Nanomagnetic Computing Is Here To Save AI’s Energy Usage

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Nanomagnetic Computing Is Here To Save AI’s Energy Usage

Nanomagnetic computing depends on nanomagnet networks for AI to make time-series predictions

“How the magnets interact gives us all the information we need; the laws of physics themselves become the computer.” Kilian Stenning quotes in his research paper “Reconfigurable training and reservoir computing in an artificial spin-vortex ice via spin-wave fingerprinting”, published in Nature magazine. He made this statement in the paper in an effort to explain how magnet spins can be put to use to power  AI systems. What is that motivated Killian and the team to apply magnetic energy to make AI predictions? Artificial intelligence, by its very versatility, has become an indispensable technology even if it is largely at the nascent stage. The interesting part is, that its acceptance is because of the predictions it can make, ignoring the carbon footprint it has. The fact is that even the simplest decision taken by AI or ML algorithm consumes a lot of energy. Higher the efficiency of the model higher the energy consumption. To put things in perspective, let us consider the example of Megatron ML, a language model similar to GPT-3, trained on 45 terabytes of data, NVIDIA had to run 512 V100 GPUs for nine days. Considering the fact that a V100 GPU consumes approximately 250 watts, the project must have required around 27,648 kWh for training, whereas an average household requires 10,649 kWh annually.

A team led by Imperial College of London researchers has found a new method of harnessing magnetic energy, to simulate the computations the conventional neural networks of an ML system undergo. The method they discovered depends on nanomagnet networks for AI to make time-series predictions. Initially, the mathematical computations required to design the neural networks were based on the principles of magnetic interactions, and now scientists have found a way to apply them directly.

 

Variability in Nanomagnetic states holds the key

A Nanomagnetic state can be understood as a spin value a particular magnet takes in an excited state.  When a group of nanomagnets is placed in an energy field, the magnets pass through different states of spin, and there is a pattern they interact with one another – scaling up the spins into nanopatterned arrays. To make a prediction, the scientists said, they designed a technique to count the number of magnets in a particular state after the field has passed through, and obtain the answer. When compared to electrical computations that consume energy whenever electrons pass through the energy field, nanomagnets’ stake in energy is negligible. Dr. Jack Gartside, the Co-first author of the study said, “We’ve been trying to crack the problem of how to input data, ask a question, and get an answer out of magnetic computing for a long time. Now we’ve proven it can be done, it paves the way for getting rid of the computer software that does the energy-intensive simulation.”

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New found possibilities for Edge AI computation

Real-time processing leveraging the power of Edge AI is going to go mainstream very soon for automation and artificial intelligence to be of any value to humanity. Edge AI, while bringing in low latency, can significantly reduce network traffic, it is crippled by challenges so overwhelming that companies do not even think about it. Edge AI depends largely on hardware, which market doesn’t have the capacity to produce the standardized units to support the energy-guzzling AI algorithms. In addition, it requires seamless hardware integration, a tedious task, that the current equipment can least handle. The nanomagnetic computation with magnets integrated into conventional computers can upscale the energy efficiency of the system significantly.

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AI Systems Are Not Humans! US Court Denies The Patent Right To Machines

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AI Systems Are Not Humans! US Court Denies The Patent Right To Machines

US law allows only ‘natural persons’ to be registered as individuals; individuals who can take an oath swearing their paternity to the product

Even as the debate around AI gaining sentience hasn’t subsided, artificial intelligence. US patent court struck down Stephen Thaler’s petition seeking a patent for the products developed by the AI system purportedly developed by him. CAFC, the US appeals court, in Thaler V. Vidal’s case ruled out the machine’s candidature to qualify as an inventor under the US Patent Act. The decision is in similar lines to the series of judgments delivered by courts from around the world for the same case. The legal tiff dates back to 2019 when he filed for a patent for the two products, an AI food container developed based on fractal geometry and a torch light. Instead of using his name in the patent document, he mentioned the neural network’s name, DABUS, claiming that the products are its brainchildren.

Back then US Patent and Trademark Office rejected his plea, reasoning that US laws allow only ‘natural persons’ to be registered as individuals; individuals who can take an oath swearing their paternity to the product, which machines or computer software are incapable of. This time the Court of Appeals, supporting the USPTO’s stand, was unambiguous in stating the obsolescence of “metaphysical matters” about “the nature of invention or rights, if any, of AI systems.” It stressed the repeated reference to the term “individuals” is very well in alignment with its interpretation. “While we do not decide whether an AI system can form beliefs, nothing in our record shows that one can, as reflected in the fact that Thaler submitted the requisite statements himself, purportedly on DABUS’ behalf,” CAFC explained. Cutting the game to chase, the judgment hinges on legal requirements which has little to do with the court’s understanding of AI’s capability.

Refusing to give up on his mission, he wants to contest the judgment in the US Supreme court. He says, “The court ignores the purpose of the Patent Act and the outcome that AI-generated inventions are now unpatentable, will have real negative social consequences.” This brings us to the question if artificial intelligence deserves patent rights and why. 

When Thaler says, negative consequences, he means the patent laws being misused under the cover of patent law. For example, AI has been responsible for inventions in the medical field which left unpatented could give leeway to idea hoggers, leaving businesses and funders discouraged to pursue research, preventing cutting-edge research to happen in the first place. Experts too are of the opinion that instead of rewriting the IP Acts completely, it would be a reasonable option to create a custom law including artificial intelligence, because, at the end of the day, AI learns by itself and makes decisions for itself. As artificial intelligence is becoming an omnipresent and inevitable part of day-to-day lives, it is highly imperative to consider if AI qualifies to be considered as an entity similar to a POSA (person with Ordinary Skills in Art). The lack of benchmarks to determine if AI’s invention is unique might be another challenge, given the AI’s ability to constantly improve itself.

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This argument finds support in Australian Court’s initial assumptions that led to a favorable judgment only to be overruled later by the appeals court. The judges considered the possibility of AI setting its goal, freedom to choose options and pathways towards the goals, and the ability to trawl for data it requires. The court did spell out why AI’s autonomy is not a misnomer, as long as artificial neural networks can choose the algorithms and interact with other networks. If only this argument could be taken forward to establish AI as a legit entity – which would probably be Thaler’s next line of argument – the question of ‘persona’ could be put to rest.

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The AI Way Of Making The Right Investment Decision: Ensuring Higher ROI

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The AI Way Of Making The Right Investment Decision: Ensuring Higher ROI

Even while acknowledging that AI application has business risks and some ethical issues

AI is having a long and highly dynamic intervention in almost every conceivable sphere of our life. It is hardly surprising then that it will also play an increasing role in the entrepreneurial domain, especially in business workflows and processes. Every organization seeking to achieve efficiency and effectiveness looks for appropriate business decisions. In the contemporary era, profitable business decisions to a great extent depend on high-tech applications, especially AI. But experience reveals that one cannot do it in a mechanical way. There is, for instance, a clear signal that approaching AI for short-term business capabilities seems to be paying more dividends than leveraging it as a research and development agency. So, the right kind of investment matters.

There is no doubt that in contemporary times there are a plethora of business strategies from which an organization can opt for ones according to its needs. Now we are well into the Information Society and the generation and maneuvering of data have become crucial for the success of any organization. But to repeat, the process of organizational change based on the adoption of new technology cannot be mechanical. Accumulation of mere data does not lead the organization anywhere as information overload is bad for the organization’s health. The AI can come to the rescue here with its up-end software to integrate the solution with the existing business architecture of a specific organization, without disturbing the business process and workforce. The best way to do it is to develop a comprehensive picture of the business process and workflows, rather than adopting what till now has been predominant, a sectoral approach in which an organization is perceived in terms of ‘slices.’ The AI-sourced ‘overall’ picture is being built up with as many components as product and/or service development, manufacturing, service operations, human resources, marketing and sales, supply chain management, risks, strategy, and so forth.

Modern organizations are invariably complex organizations. If the business process and workflows are ‘sliced’ and perceived in terms of separate compartments the outcome has a high chance of getting complicated and futile. AI algorithms are now facilitating organizational management by making available reliable structured reports based on real-time actionable data.  Various reports mention that AI adoption is highest within the product-or service-development and service-operations functions.

AI software has become so sophisticated that it is causing the end of the days of trial-and-error methods with extremely precise predictability. AI is at the same time giving rise to ‘smart’ communication with astonishing exactness for the intended users of the organization. With AI reaching such heights in organizational life and its business domain its predictive capacity is also combined with prescriptive capability, which in turn induces organizations to break the glass ceiling. According to a McKinsey’s global survey, published in a report titled The State of AI in 2020, the largest share of respondents reports revenue increases for inventory and parts optimization, pricing and promotion, customer-service analytics, and sales and demand forecasting. More than two-thirds of the respondents who report adopting each of those cases affirm that its adaptation has led to increased revenue. Over half of the respondents who report adopting each of that mention that the use of AI in those areas reduced costs. Such developments have special significance in the post-COVID 19 pandemic world and its transformation imperatives. According to an IBM report, negotiating the disastrous effects of the pandemic AI continues to enable organizations to address urgent and immediate business priorities—quickly and at scale. The report notes that AI improves a company’s cost base—augmenting human capability to motivate greater and more expansive efficiencies. It is also stated to help enhance or protect top-line revenue, experience, and engagement. The report’s six-year data series shows that COVID-19 has accelerated AI’s shift from experimental to widely adopted as a key lever of sustainable competitive advantage and profitability across businesses and industries around the world.

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Even while acknowledging that AI application has its risks and some ethical issues, the fact remains that the right investment in AI goes on to benefit an organization.

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