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How Will AI Transform The Financial Sector And Its Jobs? – Youth Ki Awaaz

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An ambassador and trained facilitator under Eco Femme (a social enterprise working towards menstrual health in south India), Sanjina is also an active member of the MHM Collective- India and Menstrual Health Alliance- India. She has conducted Menstrual Health sessions in multiple government schools adopted by Rotary District 3240 as part of their WinS project in rural Bengal. She has also delivered training of trainers on SRHR, gender, sexuality and Menstruation for Tomorrows Foundation, Vikramshila Education Resource Society, Nirdhan trust and Micro Finance, Tollygunj Women In Need, Paint It Red in Kolkata.

Now as an MH Fellow with YKA, shes expanding her impressive scope of work further by launching a campaign to facilitate the process of ensuring better menstrual health and SRH services for women residing in correctional homes in West Bengal. The campaign will entail an independent study to take stalk of the present conditions of MHM in correctional homes across the state and use its findings to build public support and political will to take the necessary action.

Saurabh has been associated with YKA as a user and has consistently been writing on the issue MHM and its intersectionality with other issues in the society. Now as an MHM Fellow with YKA, hes launched the Right to Period campaign, which aims to ensure proper execution of MHM guidelines in Delhis schools.

The long-term aim of the campaign is to develop an open culture where menstruation is not treated as a taboo. The campaign also seeks to hold the schools accountable for their responsibilities as an important component in the implementation of MHM policies by making adequate sanitation infrastructure and knowledge of MHM available in school premises.

Grafton Guineas beckon after red-hot start to July Carnival – Fox Sports

While Grafton wont be represented in the Ramornie Handicap, local hopes will be pinned on father and daughter duo, trainer Greg and jockey Leah Kilner, in todays next biggest race, the $80,000 Tursa Grafton Guineas.

Stable star Swanston has won four races and placed four times from 14 starts heading into the 1600m three-year-old feature. The gelding gained direct entry by winning the Grafton Guineas Prelude (1240m) on Westlawn Day (June 27), and will carry 55.5kg with Leah once again in the saddle.

The son of Smart Missile paid healthy odds of $21 when he kicked ahead in the straight and held off higher-fancied rivals Oakfield Arrow ($2.80fav) and Alpha Go ($8) to the line.

I thought Swanston was well over the odds, Greg said of the prelude win. Ridiculous odds on form, ran a place to a good horse (Adelaides Diamond) on the Gold Coast (May 12) on a very heavy track (heavy 10).

After the recent square off, the Ladbrokes bookmakers have put Swanston ($10) on par with Oakfield Arrow ($9.50), behind Les Kelly-trained favourite Tamilaide ($3.40), on the eve of the Grafton Guineas. However, the wider draw out of barrier ten has connections concerned.

I give him a really good each way chance, for sure, Leah said of Swanston from the familys stables at Cuban Song Lodge yesterday.

He loves Grafton, he has a really good record here, and he can run a mile.

I hope the track stays a little bit wet for him, because he likes the sting out of the ground.

Hes just drawn a bit awkward, so I dont know what were going to get. Hes a horse that does like the fence. But he tries his heart out, so hell be thereabouts.

Swanstons win completed a double for the Kilners on the opening day of the carnival, after Volfoni ($14) stormed home on the outside from the rear of the field in the Westlawn Insurance Brokers Class One Handicap (1215m).

Volfoni has also drawn wide (15) in todays last race at Clarence River Jockey Club, the Grafton Taxis Benchmark 58 Handicap (1400m).

Hes drawn extremely wide and hell have to go back, Lead said.

Hell probably be last again like he was the other day. Just let him work into it and let him find the line again.

The track has been playing a little bit leader-ish. Hopefully on the main days Im sure the rail will be back in the true and everything will get the chance.

He will be second up, hopefully he doesnt suffer from second up syndrome, but I think hell be right.

Hes a horse that really loves racing. Hes probably not blessed with the most of ability, but he just puts in 110 per cent every time he goes round. Hes a bit of a stable favourite, he just does whatever you want him to do and just tries every time to put him around.

The wins to Swanston and Volfoni almost doubled the stables 2020-21 season tally, with just three - including two to Swanston - prior to Westlawn Day.

Meanwhile, Leah is tied with Matthew McGuren as virtual leaders in the race for the Jockey of the Carnival, with a pair of second placings on Scilago in the Grafton Cup Prelude and Hit The Target in the South Grafton Cup to go with her two wins.

Its always really good to get a winner in carnival time, because everyones watching and youre a little bit more pumped up, she said.

But at the end of the day it is work, and youre trying to win every race you can.

She will ride Scilago again in the CPSU NSW Wage Growth Rural Plate Class 6 (2200m) today for Gold Coast trainer Leon Elliott.

He run really well when second in the Cup Prelude, Leah said.

They decided not to go to the cup and put him in the 2200 on Ramornie Day. Hes only a little horse but he tries really hard and its not a very strong race, so I give him a really good chance.

Ive got three really nice rides tomorrow.

The Grafton track was rated a soft six on the eve of Ramornie Day.

The first race of the day is the Winning Edge Presentations 4YO&Up Class 2 Handicap (1600m) at 12.29pm, the Guineas run at 3.24pm and the Ramornie at 3.59pm and the last at 4.34pm.

July 6th, 2021

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Anandtech: "Using AI to Build Better Processors: Google Was Just the Start, Says Synopsys" – AnandTech

In an exclusive to AnandTech, we spoke with Synopsys CEO Aart de Geus ahead of a pair of keynote presentations at two upcoming technical semiconductor industry events this year. Synopsys reached out to give us an overview of the key topic of the day, of the year:as part of these talks, Aart will discuss what was considered impossible only a few years ago the path to finding a better and automated way into chip design through the use of machine learning solutions. Within the context of EDA tools,as Google has demonstrated recently, engineers can be assisted in building better processors using machine learning algorithms.

If you read mainstream columns about technology and growth today, there is an eminent focus on the concepts of big data, artificial intelligence, and the value of analyzing that data. With enough data that has been analyzed effectively, companies have shown that they are proactive to customers,predict their needs in advance, or identify trends and react before a human has even seen the data. The more data you have analyzed, the better your actions or reactions can be. This has meant that analyzing the amount of data itself has intrinsic value, as well as the speed at which it is processed. This has causedan explosion of the demand for better analysis tools but also an explosion in data creation itself. Many senior figures in technology and business see the intersection and development of machine learning data analysis tools to churn through that data as the mark of the next generation of economics.

Graph showing manufacturing growth of key silicon product lines since 2016at TSMC, the world's largest contract manufacturer

The desire to have the best solution is accelerating the development of better utilities, but at the same time, the need to deploy it at scale is creating immense demand for resources. All the while, a number of critics are forecasting that Moores Law, a 1960s observation around the exponential development of complex computing that has held true for 50 years, is reaching its end. Othersare busy helping it to stay on track. As driving performance requires innovation on multiple levels, including hardware and software, the need to optimize every abstraction layer to continue that exponential growth has become more complex, more expensive, and requires a fundamental economic gain to those involved to continue investment.

One of the ways in driving performance on the hardware side is in designing processors to work faster and more efficiently. Two processors with the same fundamental building blocks can have those blocks placed in many different orientations, with some arrangements beneficial for power, others for performance, or perhaps for design area, while some configurations make no sense whatsoever. Finding the best combination in light of the economics at the time is often crucial to the competitiveness of the product and the buoyancy of the company that relies on the success of that product. The semiconductor industry is rare in that most chip design companies effectively bet the entire company on the success of the next generation, which makes every generation's design more important than the last.

In light of the rate of innovation, chip design teams have spent tens of thousands of hours honing their skills over decades. But we are at a stage where a modern complex processor has billions of transistors and millions of building blocks to put together in something the size of a toenail. These teams use their expertise, intuition, and nous to place these units in the best configuration, and it gets simulated over the course of 72 hours. The results that come through are analyzed, the design goes back to be updated, and the process repeats. Getting the best human-designed processor in this fashion can take six months or more, because the number of arrangements possible is equivalent to the number of atoms in the known universe risen to the power of the number of atoms in the known universe. With numbers so large, using computers to brute force the best configuration is impossible. At least, it was thought to be.

Work from Google was recently published in the scientific journal Nature about how the company is already using custom AI tools to develop better silicon, which in turn helps develop better custom AI tools. In the research paper, the company applied machine learning algorithms to find the best combination of power, performance, and die area for a number of test designs.

In order to reduce the complexity of the problem, Google limited its scope to certain layers within the design. Take, for example, an electrical circuit that is designed to add numbers together - in Googles work, rather than try and find the best way to build a circuit like this every time, they took a good adder design as a fundamental building block of the problem, mapped how it interacts with other different fundamental blocks, and then the AI software found the best way to build these fundamental blocks. This cuts down the number of different configurations needed, but the problem is still a difficult one to crack, as these blocks will interact with other blocks to varying degrees based on proximity, connections, and electrical/thermal interactions. The nature of the work always depends on what level of abstraction these different building blocks take, and how complex/basic you make them.

Simple 8-stage example of block placement and routing affects the design choices

In Googles paper, the company states that their tools have already been put to use in helping design four parts of an upcoming Google TPU processor designed for machine learning acceleration. While the paper showcases that AI tools werent used across the whole processor, it is taking some of the work that used to be painstaking in engineer labor hours and accelerating the process through computation. The beauty of this application is that the way these building blocks can be put together can scale, and companies like Google can use their datacenters to test thousands of configurations in a single day, rather than having a group of engineers provide a handful of options after several months.

Googles approach also details the effect of using optimized machine learning (so algorithms that have learned how to be better by examining previous designs) against fresh machine learning (algorithms with only a basic understanding that learn from their own trial and error). Both these areas are important, showcasing that in some circumstances, the algorithms do not need to be pre-trained but can still deliver a better-than-human result. That result still requires additional validation for effectiveness, and the results are fed back into the software team to create better algorithms.

But this is just the tip of the iceberg, according to Synopsys CEO Aart de Geus, whose company's software helps develop more silicon processing intellectual property in the industry today than anyone else. Synopsys has been involved in silicon design for over 35+ years, with hundreds of customers, and its latest AI-acceleratedproduct is already in use at a number of high-profile silicon design teams making processors today to help accelerate time to market with a better semiconductor placement than humans can achieve.

Synopsys is a company that makes EDA tools, or Electronic Design Automation, and every semiconductor company in the industry, both old and new, relies on some form of EDA to actually bring silicon to market. EDA tools allow semiconductor designers to effectively write code that describes what they are trying to make, and that can be simulated to sufficient accuracy to tell the designer if it fits within strict parameters, meets the requirements for the final manufacturing, or if it has thermal problems, or perhaps signal integrity does not meet required specifications for a given standard.

EDA tools also rely on abstraction, decades of algorithm development, and as the industry is moving to multi-chip designs and complex packaging technologies, the software teams behind these tools have to be quick to adapt to an ever-changing landscape. Having relied on complex non-linear algorithm solutions to assist designers to date, the computational requirements of EDA tools are quite substantial, and often not scalable. Thus, ultimately any significant improvement to EDA tool design is a welcome beacon in this market.

For context, the EDA tools market has two main competitors, with a combined market cap of $80B and a combined annual revenue of $6.5B. All the major foundries work with these two EDA vendors, and it is actively encouraged to stay within these toolchains, rather than to spin your own, to maintain compatibility.

Synopsys CEO Aart de Geus is set to take the keynote presentations at two upcoming technical semiconductor industry events this year: ISSCC and Hot Chips. As part of these talks, Aart will discuss what was considered impossible only a few years ago the path to finding a better and automated way into chip design through the use of machine learning solutions. Within the context of EDA tools, as Google has demonstrated publicly, engineers can be assisted in building better processors, or similarly not so many engineers are needed to build a good processor. To this point, Aarts talk at Hot Chips will be titled:

Does Artificial Intelligence Require Artificial Architects?

I spent about an hour speaking with Aart on this topic and what it means to the wider industry. The discussion would have made a great interview on the topic, although unfortunately this was just an informal discussion! But in our conversation, aside from the simple fact that machine learning can help silicon design teams optimize more variations with better performance in a fraction of the time, Aart was clear that the fundamental drive and idea of Moores Law, regardless of the exact way you want to interpret what Gordon Moore actually said, is still driving the industry forward in very much the same way that is has been the past 50 years. The difference is now that machine learning, as a cultural and industrial revolution, is enabling emergent compute architectures and designs leading to a new wave of complexity, dubbed systemic complexity.

Aart also presented to me the factual way how the semiconductor industry has evolved. At each stage of fundamental improvement, whether thats manufacturing improvement through process node lithography such as EUV or transistor architectures like FinFET or Gate-All-Around, or topical architecture innovation for different silicon structures such as high performance compute or radio frequency, we have been relying on architects and research to enable those step-function improvements. In a new era of machine learning assisted design, such as the tip of the iceberg presented by Google, new levels of innovation can emerge, albeit with a new level of complexity on top.

Aart described that with every major leap, such as moving from 200mm to 300mm wafers, or planar to FinFET transistors, or from DUV to EUV, it all relies on economics no one company can make the jump without the rest of the industry coming along and scaling costs. Aart sees the use of machine learning in chip design, for use at multiple abstraction layers, will become a de-facto benefit that companies will use as a result of the current economic situation the need to have the most optimized silicon layout for the use case required. Being able to produce 100 different configurations overnight, rather than once every few days, is expected to revolutionize how computer chips are made in this decade.

The era of AI accelerated chip design is going to be exciting. Hard work, but very exciting.

From Synopsys point of view, the goal of introducing Aart to me and having the ability to listen to his view and ask questions was to give me a flavor ahead of his Hot Chips talk in August. Synopsys has some very exciting graphs to show, one of which they have provided to me in advance below,on how its own DSO.ai software is tackling these emerging design complexities. The concepts apply to all areas of EDA tools, but this being a business, Synopsys clearly wants to show how much progress it has made in this area and what benefits it can bring to the wider industry.

In this graph, we are plotting power against wire delay. The best way to look at this graph is to start at the labeled point at the top, which says Start Point.

All of the small blue points indicate one full AI sweep of placing the blocks in the design. Over 24 hours, the resources in this test showcase over 100 different results, with the machine learning algorithm understanding what goes where with each iteration. The end result is something well beyond what the customer requires, giving them a better product.

There is a fifth point here that isn't labeled, and that is the purple dots that represent even better results. This comes from the DSO algorithm on a pre-trained network specifically for this purpose. The benefit here is that in the right circumstances, even a better result can be achieved. But even then, an untrained network can get almost to that pointas well, indicated by the best untrained DSO result.

Synopsys has already made some disclosures with customers, such as Samsung. Across four design projects, time to design optimization was reduced by 86%, from a month do days, using up to 80% fewer resources and often beating human-led design targets.

I did come away with several more questions that I hope Aart will address when the time comes.

Firstly I would like to address where the roadmaps lie in improving machine learning in chip design. It is one thing to make the algorithm that finds a potentially good result and then to scale it and produce 100s or 1000s of different configurations overnight, but is there an artificial maximum of what can be considered best, limited perhaps by the nature of the algorithm being used?

Second, Aart and I discussed Googles competition with Go Master and 18-time world champion Lee Sedol, in which Google beat the worlds best Go player 4-1 in a board game that was considered impossible only five years prior for computers to come close to the best humans. In that competition, both the Google DeepMind AI and the human player made a 1-in-10000 move, which is rare in an individual game, but one might argue is more likely to occur in human interactions. My question to Aart is whether machine learning for chip design will ever experience those 1-in-10000 moments, or rather in more technical terms, would the software still be able to find a best global minimum if it gets stuck in a local minimum over such a large (1 in 102500 combinations for chip design vs 1 in 10230 in Go) search space.

Third, and perhaps more importantly, is how applying machine learning at different levels of the design can violate those layers. Most modern processor design relies on specific standard cells and pre-defined blocks there will be situations where modified versions of those blocks might be better in some design scenarios when coupled close to different parts of the design. With all of these elements interacting with each other and having variable interaction effects, the complexity is in managing these interactions within the machine learning algorithms in a time-efficient way, but how these tradeoffs are made is still a point to prove.

In my recent interview with Jim Keller, I asked him if at one point we will see silicon design look unfathomable to even the best engineers he said Yeah, and its coming pretty fast. It is one thing to talk holistically about what AI can bring to the world, but its another to have it working in action to improve semiconductor design and providing a fundamental benefit at the base level of all silicon. Im looking forward to further disclosures on AI-accelerated silicon design from Synopsys, its competitors, and hopefully some insights from those that are using it to design their processors.

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Anandtech: "Using AI to Build Better Processors: Google Was Just the Start, Says Synopsys" - AnandTech

June 28th, 2021

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Computer scientists are questioning whether Alphabets DeepMind will ever make A.I. more human-like – CNBC

David Silver, leader of the reinforcement learning research group at DeepMind, being awarded an honorary "ninth dan" professional ranking for AlphaGo.

JUNG YEON-JE | AFP | Getty Images

Computer scientists are questioning whether DeepMind, the Alphabet-owned U.K. firm that's widely regarded as one of the world's premier AI labs, will ever be able to make machines with the kind of "general" intelligence seen in humans and animals.

In its quest for artificial general intelligence, which is sometimes called human-level AI, DeepMind is focusing a chunk of its efforts on an approach called "reinforcement learning."

This involves programming an AI to take certain actions in order to maximize its chance of earning a reward in a certain situation. In other words, the algorithm "learns" to complete a task by seeking out these preprogrammed rewards. The technique has been successfully used to train AI models how to play (and excel at) games like Go and chess. But they remain relatively dumb, or "narrow." DeepMind's famous AlphaGo AI can't draw a stickman or tell the difference between a cat and a rabbit, for example, while a seven-year-old can.

Despite this, DeepMind, which was acquired by Google in 2014 for around $600 million, believes that AI systems underpinned by reinforcement learning could theoretically grow and learn so much that they break the theoretical barrier to AGI without any new technological developments.

Researchers at the company, which has grown to around 1,000 people under Alphabet's ownership, argued in a paper submitted to the peer-reviewed Artificial Intelligence journal last month that "Reward is enough" to reach general AI. The paper was first reported by VentureBeat last week.

In the paper, the researchers claim that if you keep "rewarding" an algorithm each time it does something you want it to, which is the essence of reinforcement learning, then it will eventually start to show signs of general intelligence.

"Reward is enough to drive behavior that exhibits abilities studied in natural and artificial intelligence, including knowledge, learning, perception, social intelligence, language, generalization and imitation," the authors write.

"We suggest that agents that learn through trial and error experience to maximize reward could learn behavior that exhibits most if not all of these abilities, and therefore that powerful reinforcement learning agents could constitute a solution to artificial general intelligence."

Not everyone is convinced, however.

Samim Winiger, an AI researcher in Berlin, told CNBC that DeepMind's "reward is enough" view is a "somewhat fringe philosophical position, misleadingly presented as hard science."

He said the path to general AI is complex and that the scientific community is aware that there are countless challenges and known unknowns that "rightfully instill a sense of humility" in most researchers in the field and prevent them from making "grandiose, totalitarian statements" such as "RL is the final answer, all you need is reward."

DeepMind told CNBC that while reinforcement learning has been behind some of its most well-known research breakthroughs, the AI technique accounts for only a fraction of the overall research it carries out. The company said it thinks it's important to understand things at a more fundamental level, which is why it pursues other areas such as "symbolic AI" and "population-based training."

"In somewhat typical DeepMind fashion, they chose to make bold statements that grabs attention at all costs, over a more nuanced approach," said Winiger. "This is more akin to politics than science."

Stephen Merity, an independent AI researcher, told CNBC that there's "a difference between theory and practice." He also noted that "a stack of dynamite is likely enough to get one to the moon, but it's not really practical."

Ultimately, there's no proof either way to say whether reinforcement learning will ever lead to AGI.

Rodolfo Rosini, a tech investor and entrepreneur with a focus on AI, told CNBC: "The truth is nobody knows and that DeepMind's main product continues to be PR and not technical innovation or products."

Entrepreneur William Tunstall-Pedoe, who sold his Siri-like app Evi to Amazon, told CNBC that even if the researchers are correct "that doesn't mean we will get there soon, nor does it mean that there isn't a better, faster way to get there."

DeepMind's "Reward is enough" paper was co-authored by DeepMind heavyweights Richard Sutton and David Silver, who met DeepMind CEO Demis Hassabis at the University of Cambridge in the 1990s.

"The key problem with the thesis put forth by 'Reward is enough' is not that it is wrong, but rather that it cannot be wrong, and thus fails to satisfy Karl Popper's famous criterion that all scientific hypotheses be falsifiable," said a senior AI researcher at a large U.S. tech firm, who wished to remain anonymous due to the sensitive nature of the discussion.

"Because Silver et al. are speaking in generalities, and the notion of reward is suitably underspecified, you can always either cherry pick cases where the hypothesis is satisfied, or the notion of reward can be shifted such that it is satisfied," the source added.

"As such, the unfortunate verdict here is not that these prominent members of our research community have erred in any way, but rather that what is written is trivial. What is learned from this paper, in the end? In the absence of practical, actionable consequences from recognizing the unalienable truth of this hypothesis, was this paper enough?"

While AGI is often referred to as the holy grail of the AI community, there's no consensus on what AGI actually is. One definition is it's the ability of an intelligent agent to understand or learn any intellectual task that a human being can.

But not everyone agrees with that and some question whether AGI will ever exist. Others are terrified about its potential impacts and whether AGI would build its own, even more powerful, forms of AI, or so-called superintelligences.

Ian Hogarth, an entrepreneur turned angel investor, told CNBC that he hopes reinforcement learning isn't enough to reach AGI. "The more that existing techniques can scale up to reach AGI, the less time we have to prepare AI safety efforts and the lower the chance that things go well for our species," he said.

Winiger argues that we're no closer to AGI today than we were several decades ago. "The only thing that has fundamentally changed since the 1950/60s, is that science-fiction is now a valid tool for giant corporations to confuse and mislead the public, journalists and shareholders," he said.

Fueled with hundreds of millions of dollars from Alphabet every year, DeepMind is competing with the likes of Facebook and OpenAI to hire the brightest people in the field as it looks to develop AGI. "This invention could help society find answers to some of the world's most pressing and fundamental scientific challenges," DeepMind writes on its website.

DeepMind COO Lila Ibrahim said on Monday that trying to "figure out how to operationalize the vision" has been the biggest challenge since she joined the company in April 2018.

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Computer scientists are questioning whether Alphabets DeepMind will ever make A.I. more human-like - CNBC

June 21st, 2021

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Chinese AI Learns To Beat Top Fighter Pilot In Simulated Combat – Forbes

A Chinese AI system has defeated a top human pilot in a simulated dogfight, according to Chinese media. The AI was pitted against Fang Guoyu, a Group Leader in a PLA aviation brigade and a previous champion in such contests.

"At first, it was not difficult to win against the AI," said Fang in a report in Global Times, a Chinese state newspaper. But as the exercise continued the AI learned from each encounter and steadily improved. By the end it was able to defeat Fang using tactics it had learned from him, coupled with inhuman speed and precision.

"The AI has shown adept flight control skills and errorless tactical decisions, said brigade commander Du Jianfeng.

The Chinese exercise of setting human pilots against AI aims to improve both. The AI gives the pilots a new and challenging opponent which thinks out of the box and can come up with unexpected tactics, while each dogfight adds to the AIs experience and helps it improve.

The AI was developed by a number of unspecified research institutes working with the aviation brigade, according to the report.

In the culmination of DARPA's AlphaDogfight exercise, the Falco AI decisively beat a skilled human ... [+] pilot in simulated combat between F-16s.

The event echoes DARPAs AlphaDogfight competition last year which featured human and AI pilots fighting it out in simulated F-16s. In the initial rounds, different AIs competed to find the best. In the final round, the winning AI, Falco from Heron Systems, took on the human champion, an unnamed U.S. Air Force pilot. The AI triumphed, scoring a perfect 5-0 win in a series of encounters.

AIs have significant advantages in this situation. One is that they are fearless and highly aggressive compared to human pilots; another term might be reckless. They can react faster than any human, and can track multiple aircraft in all directions, identifying the greatest threats and the best targets in a rapidly changing situation. They also have faster and more precise control: Falco was notably skilled at taking aim and unleashing a stream of simulated cannon fire at opponents who were still lining up their shot. Whether these advantages would carry over into a messy real-world environment is open to question further planned exercises by DARPA, the USAF and others may help settle the matter.

DARPAs ACES program, of which AlphaDogfight was part, plans to port dogfighting algorithms onto small drones and test various scenarios of one-on-one, one-versus-two, and two-versus-two encounters in the next year. At the same time they are also preparing for combat autonomy on a full-scale aircraft. This may utilize existing dumb QF-16 target aircraft, the drone versions of F-16s used for air-to-air combat practice.

The QF-16, an unmanned version of the F-16 used as an aerial target, could be upgraded to a ... [+] dogfighter with smart software

The contest for AI supremacy between the U.S. and China is attracting increasing attention, with the National Security Commission on AI (NSCAI) concluding in March that, for the first time since World War II, Americas technological predominance is under threat. China has created hundreds of new AI professorships and developed an efficient ecosystem for AI start-ups with tax breaks and lucrative government contracts on offer.

AI fighter pilots are just a tiny piece in the military balance, and not a meaningful indicator on their own. However, the fact that China chooses to publicize the latest development sends a message that they are hard on Americas heels, if not drawing ahead, in direct military applications of AI. If their AI can really learn skills that rapidly from contests with human pilots, then, like DeepMind's AlphaGo, it may now be competing with versions of itself and developing tactics and levels of skill impossible for humans.

Meanwhile, in the larger evolutionary contest between humans and AIs, the machines have just taken another tiny step forward in chipping away our superiority. The new Top Gun movie out later this year may be nostalgic on more ways than one.

Continued here:
Chinese AI Learns To Beat Top Fighter Pilot In Simulated Combat - Forbes

June 21st, 2021

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Mediaboss Marketing

Archive for the ‘Alphago’ Category

How Will AI Transform The Financial Sector And Its Jobs? – Youth Ki Awaaz

Grafton Guineas beckon after red-hot start to July Carnival – Fox Sports

Anandtech: "Using AI to Build Better Processors: Google Was Just the Start, Says Synopsys" – AnandTech

Computer scientists are questioning whether Alphabets DeepMind will ever make A.I. more human-like – CNBC

Chinese AI Learns To Beat Top Fighter Pilot In Simulated Combat – Forbes

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