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Examining the world through signals and systems – MIT News

Posted: February 10, 2021 at 9:52 pm

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Theres a mesmerizing video animation on YouTube of simulated, self-driving traffic streaming through a six-lane, four-way intersection. Dozens of cars flow through the streets, pausing, turning, slowing, and speeding up to avoid colliding with their neighbors. And not a single car stopping. But what if even one of those vehicles was not autonomous? What if only one was?

In the coming decades, autonomous vehicles will play a growing role in society, whether keeping drivers safer, making deliveries, or increasing accessibility and mobility for elderly or disabled passengers.

But MIT Assistant Professor Cathy Wu argues that autonomous vehicles are just part of a complex transport system that may involve individual self-driving cars, delivery fleets, human drivers, and a range of last-mile solutions to get passengers to their doorstep not to mention road infrastructure like highways, roundabouts, and, yes, intersections.

Transport today accounts for about one-third of U.S. energy consumption. The decisions we make today about autonomous vehicles could have a big impact on this number ranging from a 40 percent decrease in energy use to a doubling of energy consumption.

So how can we better understand the problem of integrating autonomous vehicles into the transportation system? Equally important, how can we use this understanding to guide us toward better-functioning systems?

Wu, who joined the Laboratory for Information and Decision Systems (LIDS) and MIT in 2019, is the Gilbert W. Winslow Assistant Professor of Civil and Environmental Engineering as well as a core faculty member of the MIT Institute for Data, Systems, and Society. Growing up in a Philadelphia-area family of electrical engineers, Wu sought a field that would enable her to harness engineering skills to solve societal challenges.

During her years as an undergraduate at MIT, she reached out to Professor Seth Teller of the Computer Science and Artificial Intelligence Laboratory to discuss her interest in self-driving cars.

Teller, who passed away in 2014, met her questions with warm advice, says Wu. He told me, If you have an idea of what your passion in life is, then you have to go after it as hard as you possibly can. Only then can you hope to find your true passion.

Anyone can tell you to go after your dreams, but his insight was that dreams and ambitions are not always clear from the start. It takes hard work to find and pursue your passion.

Chasing that passion, Wu would go on to work with Teller, as well as in Professor Daniela Russ Distributed Robotics Laboratory, and finally as a graduate student at the University of California at Berkeley, where she won the IEEE Intelligent Transportation Systems Society's best PhD award in 2019.

In graduate school, Wu had an epiphany: She realized that for autonomous vehicles to fulfill their promise of fewer accidents, time saved, lower emissions, and greater socioeconomic and physical accessibility, these goals must be explicitly designed-for, whether as physical infrastructure, algorithms used by vehicles and sensors, or deliberate policy decisions.

At LIDS, Wu uses a type of machine learning called reinforcement learning to study how traffic systems behave, and how autonomous vehicles in those systems ought to behave to get the best possible outcomes.

Reinforcement learning, which was most famously used by AlphaGo, DeepMinds human-beating Go program, is a powerful class of methods that capture the idea behind trial-and-error given an objective, a learning agent repeatedly attempts to achieve the objective, failing and learning from its mistakes in the process.

In a traffic system, the objectives might be to maximize the overall average velocity of vehicles, to minimize travel time, to minimize energy consumption, and so on.

When studying common components of traffic networks such as grid roads, bottlenecks, and on- and off-ramps, Wu and her colleagues have found that reinforcement learning can match, and in some cases exceed, the performance of current traffic control strategies. And more importantly, reinforcement learning can shed new light toward understanding complex networked systems which have long evaded classical control techniques. For instance, if just 5 to 10 percent of vehicles on the road were autonomous and used reinforcement learning, that could eliminate congestion and boost vehicle speeds by 30 to 140 percent. And the learning from one scenario often translates well to others. These insights could one day soon help to inform public policy or business decisions.

In the course of this research, Wu and her colleagues helped improve a class of reinforcement learning methods called policy gradient methods. Their advancements turned out to be a general improvement to most existing deep reinforcement learning methods.

But reinforcement learning techniques will need to be continually improved to keep up with the scale and shifts in infrastructure and changing behavior patterns. And research findings will need to be translated into action by urban planners, auto makers and other organizations.

Today, Wu is collaborating with public agencies in Taiwan and Indonesia to use insights from her work to guide better dialogues and decisions. By changing traffic signals or using nudges to shift drivers behavior, are there other ways to achieve lower emissions or smoother traffic?

Im surprised by this work every day, says Wu. We set out to answer a question about self-driving cars, and it turns out you can pull apart the insights, apply them in other ways, and then this leads to new exciting questions to answer.

Wu is happy to have found her intellectual home at LIDS. Her experience of it is as a very deep, intellectual, friendly, and welcoming place. And she counts among her research inspirations MIT course 6.003 (Signals and Systems) a class she encourages everyone to take taught in the tradition of professors Alan Oppenheim (Research Laboratory of Electronics) and Alan Willsky (LIDS). The course taught me that so much in this world could be fruitfully examined through the lens of signals and systems, be it electronics or institutions or society, she says. I am just realizing as Im saying this, that I've been empowered by LIDS thinking all along!

Research and teaching through a pandemic havent been easy, but Wu is making the best of a challenging first year as faculty. (Ive been working from home in Cambridge my short walking commute is irrelevant at this point, she says wryly.) To unwind, she enjoys running, listening to podcasts covering topics ranging from science to history, and reverse-engineering her favorite Trader Joes frozen foods.

Shes also been working on two Covid-related projects born at MIT: One explores how data from the environment, such as data collected by internet-of-things-connected thermometers, can help identify emerging community outbreaks. Another project asks if its possible to ascertain how contagious the virus is on public transport, and how different factors might decrease the transmission risk.

Both are in their early stages, Wu says. We hope to contribute a bit to the pool of knowledge that can help decision-makers somewhere. Its been very enlightening and rewarding to do this and see all the other efforts going on around MIT.

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Examining the world through signals and systems - MIT News

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February 10th, 2021 at 9:52 pm

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Are we ready for bots with feelings? Life Hacks by Charles Assisi – Hindustan Times

Posted: December 12, 2020 at 7:54 am

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In the 2008 Pixar film Wall-E, a robot thats operated alone for centuries meets and falls in love with another bot, starting a relationship with serious implications for mankind.

Does my phone know when Ive exchanged it and moved on to a new one? Does it work better with its new owner because he follows its instructions when he drives, meaning that it doesnt have to scramble to do one more task while its processor was focused on something else? Does the contraption prefer my kids over me?! They seem to have a lot more fun together.

As a species, we tend to anthropomorphise objects a lamp looks cute, a couch looks sad but what do we do with objects to which we have given a kind of operational intelligence, enough at least to operate independently of us? How do we view them? What standing do they have, relative to the lamp and the couch?

The idea that it might be time to start thinking about rights and status for artificial intelligence was explored last month in a lovely essay on the moral implications of building true artificial intelligence, written by Anand Vaidya, professor of philosophy at San Jose University, and published on the academic news portal The Conversation.

His attempt to place things in perspective begins with a question. What is the basis upon which something has rights? What gives an entity moral standing?

That my phone has a kind of intelligence is obvious because the answers that the voice assistant comes up with in response to questions are often indistinguishable from how a human might answer. But this is rather basic. Science has been at work to push those boundaries. Three years ago, an algorithm called AlphaGo taught itself to play chess until it beat the grandmaster Garry Kasparov. A very gracious Kasparov applauded the algorithm and called its win a victory for humankind.

Advances such as these place in perspective why my younger daughter sneaks away with my phone when she thinks no one is looking, as if running off with a friend. She asks Siri to play her a song, tell her a joke, help with her homework. The algorithm powering the device does all that, and rather nonchalantly. When looked at from a distance, it appears, they bond, my daughter and the bot.

Now, it is broadly agreed that rights are to be granted only to beings with a capacity for consciousness. Thats why animals have rights, in our systems of justice, and not hills or rocks.

It is also generally agreed that there are two types of consciousness. One is linked to the experience of phenomena the scent of a rose, the prick of a thorn. Our devices are bereft of this phenomenal consciousness.

But they do have what is called access consciousness, Vaidya points out. In the same way that you can automatically catch and pass a ball mid-game on reflex, a smart device can alert me when it is low on battery and suggest I recharge, save my work, switch to another device.

As the algorithms that allow it to do that evolve, and artificial intelligence gets smarter, developing even more advanced forms of this access consciousness, it is conceivable that a future algorithm will interact very differently with a younger user than with an adult. That it will know one from the other more specifically.

Isnt it time then that we started thinking about creating a code of conduct around how we will interact with such devices, how we will allow them to interact with each other, and at what points we will intervene to control, moderate, or terminate?

I believe it is time we started thinking about these ethical voids. Because the AI of science fiction is still in the future, but we can feel it getting closer all the time.


Are we ready for bots with feelings? Life Hacks by Charles Assisi - Hindustan Times

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December 12th, 2020 at 7:54 am

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What are proteins and why do they fold? – DW (English)

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The proteins in our bodies are easily confused with the proteinin food.There are similarities and links between the two for example, both consist of amino acids.

But, when scientists talk about proteins in biology, they are talking about tiny butcomplex molecules that perform a huge range of functions at a cellular level, keeping us healthy and functioning as a whole.

Scientists will often talk about proteins "folding" and say that when they fold properly, we're OK. The way they fold determines their shape, or 3D structure, and that determines their function.

But, when proteins fail to fold properly, they malfunction, leaving us susceptible to potentially life-threatening conditions.

We don't fully understand why: why proteins fold and how, and why it doesn't always work out.

When proteins go wrong: 'Lewy bodies' or protein deposits in neurons can lead to Parkinson's cisease

The whole thing has been bugging biologists for 50 or 60 years, with three questions summarized as the "protein-folding problem."

It appears that that final question has now been answered, at least in part.

An artificial intelligence systemknown as AlphaFold can apparently predict the structure of proteins.

AlphaFold is a descendant of AlphaGo a gaming AI that beat human GO champion Lee Sedol in 2016. GO is a game like chess but tougher to the power of 10.

DeepMind,the company behind AlphaFold, is calling it a "major scientific advance."

To be fair, it's not the first time that scientists have reported they have used computer modeling to predict the structure of proteins;they have done that for a decade or more.

Perhaps it's the scale that AI brings to the field the ability to do more, faster. DeepMind say they hope to sequence the human proteome soon, the same way that scientists sequenced the human genome and gave us all our knowledge about DNA.

But why do it? What is it about proteins that makes them so important for life?

Well, predicting protein structure may help scientists predict your health for instance, the kinds of cancer you may or may not be at risk of developing.

Proteins are indeed vital for life they are like mechanical components, such ascogs in a watch or strings and keys in a piano.

Proteins form when amino acids connect in a chain. And that chain "folds" into a 3D structure. When it fails to fold, it forms a veritable mess a sticky lump of dysfunctional nothing.

Proteins can lend strength to muscle cells, or form neurons in the brain.The US National Institutes of Health lists five main groups of proteins and their functions:

There can be between 20,000 and 100,000 unique types of proteins within a human cell. They form out of an average of 300 amino acids, sometimes referred to as protein building blocks. Each is a mix of the 22 differentknown amino acids.

Those amino acids are chained together, and the sequence, or order, of that chain determines how the protein folds upon itselfand, ultimately, its function.

Protein-folding can be a process of hit-and-miss. It's a four-part process that usually begins with twobasic folds.

Healthy proteins depend on a specific sequence of amino acids and how the molecule 'folds' and coils

First, parts of a protein chain coil up into what areknown as "alpha helices."

Then, other parts or regions of the protein form "beta sheets," which look a bit like the improvised paper fans we make on a hot summer's day.

In steps three and four, you get more complex shapes. The two basic structures combine into tubes and other shapes that resemble propellers, horseshoes or jelly rolls. And that gives them their function.

Tube or tunnel-like proteins, for instance, can act as an express route for traffic to flow in and out of cells. There are "coiled coils" that move like snakes to enable a function in DNA clearly, it takes all types in the human body.

Successful protein folding depends on a number of things, such as temperature, sufficient space in a celland, it is said, even electrical and magnetic fields.

Temperature and acidity (pH values) in a cell, for instance, can affect the stability of a protein its ability to hold its shape and therefore perform its correct function.

Chaperone proteins can assist other proteins while folding and help mitigate bad folding. But it doesn't always work.

Misfolded proteins are thought to contribute to a range of neurological diseases, including Alzheimer's, Parkinson's andHuntington's diseaseand ALS.

It's thought that when a protein fails to foldand perform a specific function, known as "loss of function," that specific job just doesn't get done.

As a result, cells can get tired for instance, when a protein isn't there to give them the energy they need and eventually they get sick.

Researchers have been trying to understand why some proteins misfold more than others, why chaperones sometimes fail to help, and why exactly misfolded proteins cause the diseases they are believed to cause.

Who knows? DeepMind's AlphaFold may help scientists answer these questions a lot faster now. Or throw up even more questions to answer.

Bugs can be tasty. So why is it that we don't we eat more of them? There are plenty of reasons to do so: insects are easy to raise and consume fewer resources than cows, sheep or pigs. They dont need pastures, they multiply quickly and they don't produce greenhouse gasses.

Water bugs, scorpions, cockroaches - on a stick or fried to accompany beer: these are delicacies in Asia, and healthy ones at that. Insects, especially larvae, are an energy and protein bomb. One hundred grams of termites, for example, have 610 calories - more than chocolate! Add to that 38 grams of protein and 46 grams of fat.

Insects are full of unsaturated fatty acids, iron, vitamins and minerals says the UNS Food and Agriculture Organisation (FAO). The organization wants to increase the popularity of insect recipes around the world.

In many countries around the world, insects have long been a popular treat, especially in parts of Asia, Africa and Latin America. Mopane caterpillars, like the ones shown here, are a delicacy in southern Africa. They're typically boiled, roasted or grilled.

Even international fine cuisine features insects. And in Mexican restaurants, worms with guacamole are a popular snack. Meanwhile, new restaurants in Germany are starting to pop up that offer grasshoppers, meal worms and caterpillars to foodies with a taste for adventure.

In Europe and America, beetles, grubs, locusts and other creepy crawlers are usually met with a yuck! The thought of eating deep-fried tarantulas, a popular treat in Cambodia, is met with great disgust. But is there a good reason for that response?

Fine food specialists Terre Exotique (Exotic Earth) offer a grilled grasshopper snack. The French company currently sells the crunchy critters online via special order. A 30-gram jar goes for $11.50 (9 euros).

There are about 1,000 edible insect varieties in the world. Bees are one of them. They're a sustainable source of nutrition, full of protein and vitamins - and tasty for the most part. The world needs to discover this delicacy, says the UN's Food and Agriculture Organization.

In 2012, researchers used ecological criteria to monitor mealworm production at an insect farm in the Netherlands. The result? For the production of one kilogram of edible protein, worm farms use less energy and much less space than dairy or beef farms.

Even in Germany, insects used to be eaten in abundance. May beetle soup was popular until the mid-1900s. The taste has been described as reminiscent of crab soup. In addition, beetles were sugared or candied, then sold in pastry shops.

French start-up Ynsect is cooking up plans to offer ground up mealworms as a cost-effective feed for animals like fish, chicken and pigs. This could benefit the European market, where 70 percent of animal feed is imported.

Author: Lori Herber

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What are proteins and why do they fold? - DW (English)

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December 12th, 2020 at 7:53 am

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Are Computers That Win at Chess Smarter Than Geniuses? – Walter Bradley Center for Natural and Artificial Intelligence

Posted: December 4, 2020 at 5:52 am

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Big computers conquered chess quite easily. But then there was the Chinese game of go (pictured), estimated to be 4000 years old, which offers more degrees of freedom (possible moves, strategy, and rules) than chess (210170). As futurist George Gilder tells us, in Gaming AI, it was a rite of passage for aspiring intellects in Asia: Go began as a rigorous rite of passage for Chinese gentlemen and diplomats, testing their intellectual skills and strategic prowess. Later, crossing the Sea of Japan, Go enthralled the Shogunate, which brought it into the Japanese Imperial Court and made it a national cult. (p. 9)

Then AlphaGo, from Googles DeepMind, appeared on the scene in 2016:

As the Chinese American titan Kai-Fu Lee explains in his bestseller AI Super-powers,8 the riveting encounter between man and machine across the Go board had a powerful effect on Asian youth. Though mostly unnoticed in the United States, AlphaGos 2016 defeat of Lee Sedol was avidly watched by 280 million Chinese, and Sedols loss was a shattering experience. The Chinese saw DeepMind as an alien system defeating an Asian man in the epitome of an Asian game.

Thirty-three-year-old Korean Lee Se-dol later announced his retirement from the game. Meanwhile, Gilder tells us, that defeat, plus a later one, sparked a huge surge in Chinese investment in AI in response: Less than two months after Ke Jies defeat, the Chinese government launched an ambitious plan to lead the world in artificial intelligence by 2030. Within a year, Chinese venture capitalists had already surpassed US venture capitalists in AI funding.

AI went on to conquer poker, Starcraft II, and virtual aerial dogfights.

The machines won because improvements in machine learning techniques such as reinforcement learning enable much more effective data crunching. In fact, soon after the defeats of human go champions, a more sophisticated machine was beating a less sophisticated machine at go. As Gilder tells it, in 2017, Googles DeepMind launched AlphaGo Zero. Using a generic adversarial program, AlphaGo Zero played itself billions of times and then went on to defeat AlphaGo 1000 (p. 11). This incident went largely unremarked because it was a mere conflict between machines.

But what has really happened with computers, humans, and games is not what we are sometimes urged to think, that machines are rapidly developing human-like capacities. In all of these games, one feature stands out: The map is the territory.

Think of a simple game like checkers. There are 64 squares and each of two players is given 12 pieces. Each player tries to eliminate the other players pieces from the board, following the rules. Essentially, in checkers, there is nothing beyond the pieces, the board, and the official rules. Like go, its a map and a territory all in one.

Games like chess, go, and poker are vastly more complex than checkers in their degrees of freedom. But they all resemble checkers in one important way: In all cases, the map is the territory. And that limits the resemblance to reality. As Gilder puts it, Go is deterministic and ergodic; any specific arrangement of stones will always produce the same results, according to the rules of the game. The stones are at once symbols and objects; they are always mutually congruent. (pp 5051)

In other words, the structure of a game rules out, by definition, the very types of events that occur constantly in the real world where, as many of us have found reason to complain, the map is not the territory.

Or, as Gilder goes on to say in Gaming AI,

Plausible on the Go board and other game arenas, these principles are absurd in real world situations. Symbols and objects are only roughly correlated. Diverging constantly are maps and territories, population statistics and crowds of people, climate data and the actual weather, the word and the thing, the idea and the act. Differences and errors add up as readily and relentlessly on gigahertz computers as lily pads on the famous exponential pond.

Generally, AI succeeds wherever the skill required to win is calculation and the territory is only a map. For example, take IBM Watsons win at Jeopardy in 2011. As Larry L. Linenschmidt of Hill Country Institute has pointed out, Watson had, it would seem, a built-in advantage then by having infinitemaybe not infinite but virtually infiniteinformation available to it to do those matches.

Indeed. But Watson was a flop later in clinical medicine. Thats probably because computers only calculate and not everything in the practice of medicine in a real-world setting is a matter of calculation.

Not every human intellectual effort involves calculation. Thats why increases in computing power cannot solve all our problems. Computers are not creative and they do not tolerate ambiguity well. Yet success in the real world consists largely in mastering these non-computable areas.

Science fiction has dreamed that ramped-up calculation will turn computers into machines that can think like humans. But even the steepest, most impressive calculations do not suddenly become creativity, for the same reasons as maps do not suddenly become the real-world territory. To think otherwise is to believe in magic.

Note: George Gilders book, Gaming AI, is free for download here.

You may also enjoy: Six limitations of artificial intelligence as we know it. Youd better hope it doesnt run your life, as Robert J. Marks explains to Larry Linenschmidt.

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Are Computers That Win at Chess Smarter Than Geniuses? - Walter Bradley Center for Natural and Artificial Intelligence

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December 4th, 2020 at 5:52 am

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An AI winter may be inevitable. What we should fear more: an AI ice age – ITProPortal

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In The Queens Gambit, the recent Netflix drama on a chess genius, the main character is incredibly focused and driven. You might even say machine-like. Perhaps, you could go so far as to say shes a little bit like an incredibly single-minded Artificial Intelligence (AI) program like AlphaGo.

Hoping not to give any spoilers here, but in the drama, Beth eventually succeeds not just because shes a chess prodigy, able to see the board many moves ahead. She succeeds because she teams up with fellow players who give her hints and tips on the psychology and habits of her main Big Boss opponent.

In other words, she employs tactics, strategy, reasoning and planning; she sees more than the board. She reads the room, one might say. Emotions play a huge part in all she does and is key to her eventual triumph in Moscow.

And this is why were potentially in a lot of trouble in AI. AlphaGo cant do any of what Beth and her friends do. Its a brilliant bit of software, but its an idiot savantall it knows is Go.

Right now, very few people care about that. Which is why I fear we may be headed not just into another AI Winter, but an almost endless AI Ice Age, perhaps decades of rejection of the approach, all the VC money taps being turned off, lack of Uni research fundingall the things we saw in the first AI Winter of 1974-80 and the second of 1987-93.

Only much, much worse.

Im also convinced the only way to save the amazing achievements weve seen with programs like AlphaGo is to make them more like Bethable to see much, much more than just the board in front of them.

Lets put all this in context. Right now, we are without doubt enjoying the best period AI has ever had. Years and years of hard backroom slog at the theoretical level has been accompanied by superb improvements in hardware performancea combination that raised our game really without us asking. Hence todays undoubted AI success story: Machine Learning. Everyone is betting on this approach and its roots in Deep Learning and Big Data, which is fine; genuine progress and real applications are being delivered at the moment.

But theres a hard stop coming. One of the inherent issues for Deep Learning is you need bigger and bigger neural network and parameters to achieve more than you did last time, and so you soon end up with incredible numbers of parameters: the full version of GPT-3 has 175 billion. But to train those size of networks takes immense computational powerand even though Moores Law continues to be our friend, even that has limits. And were set to reach them a lot sooner than many would like to think.

Despite its reputation for handwaving and love of unobtanium, the AI field is full of realists. Most have painful memories of what happened the last time AIs promise met intractable reality, a cycle which gives rise to the concept of the AI Winter. In the UK, in 1973 a scathing analysisthe infamous Lighthill Reportconcluded that AI just wasnt worth putting any more money into. Similarly fed up, once amazingly generous Defence paymasters in the US ended the first heuristic-search based boom, and the field went into steep decline until the expert systems/knowledge engineering explosion of the 1980s, which also, eventually, also went cold when to many over-egged promises met the real world.

To be clear, both periods provided incredible advances, including systems that saved money for people and improved industries. AI never goes away, either; anyone working in IT knows that theres always advanced programming and smart systems somewhere helping outwe dont even call them AI anymore, they just work without issue. So on one hand, AI wont stop, even if it goes out of fashion once again; getting computers and robots to help us is just too useful an endeavour to stop.

But what will happen is an AI Winter that will follow todays boom. Sometime soon, data science will stop being fashionable; ML models will stop being trusted; entrepreneurs offering the City a Deep Learning solution to financial problem X wont have their emails returned.

And what might well happen beyond that is even worse not just a short period of withdrawal of interest, but a deep, deep freeze10, 20, 30 years long. I dont want to see that happen, and thats just not because I love AI or want my very own HAL 9000 (though, course I doso do you). I dont want to see it happen because I know that Artificial Intelligence is real, and while there may be genuinely fascinating philosophical arguments for and against it, eventually we will create something that can do things as well as humans can.

But note that I said things. AlphaGo is better than all of us (certainly me) at playing games. Google Translate is better than me at translating multiple languages, and so on. What we need are smart systems that are better at more than one thing can start being intelligent, even at very low levels, outside a very narrow domain. Step forward AGI, Artificial General Intelligence, which are suites of programs that apply intelligence to a wide variety of problems, in much the same ways humans can.

We're only seeing the most progress in learning because that's where all the investment is going

For example: weve only been focused on learning the last 15 years. But AI done properly needs to cover a range of intelligence capabilities, of which being able to learn and spot patterns is just one; there's reasoning, there's understanding, there's a lot of other types of intelligence capabilities that should be part of an overall Artificial Intelligence practice or capability.

We know why that iswere focused on learning because we got good traction with that and made solid progress. But there's all the other AI capabilities that we should be also looking at and investigating, and were just not. Its a Catch-22: all the smart money is going into Machine Learning because that's where we're seeing the most progress, but we're only seeing the most progress in learning because that's where all the investment is going!

To sum up, then: we may not be able to stave off a Machine Learning AI Winter; perhaps its an inevitable cycle. But to stave off an even worse and very, very destructive AI Ice Age, I think we need to widen the focus here, get AGI back on its feet, and help our Beths get better at a lot more than just chess or were just going to see them turned off, one by one.

Andy Pardoe, founder and managing director, Pardoe Ventures

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An AI winter may be inevitable. What we should fear more: an AI ice age - ITProPortal

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December 4th, 2020 at 5:52 am

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What the hell is reinforcement learning and how does it work? – The Next Web

Posted: November 2, 2020 at 1:56 am

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Reinforcement learning is a subset of machine learning. It enables an agent to learn through the consequences of actions in a specific environment. It can be used to teach a robot new tricks, for example.

Reinforcement learning is a behavioral learning model where the algorithm provides data analysis feedback, directing the user to the best result.

It differs from other forms of supervised learning because the sample data set does not train the machine. Instead, it learns by trial and error. Therefore, a series of right decisions would strengthen the method as it better solves the problem.

Reinforced learning is similar to what we humans have when we are children. We all went through the learning reinforcement when you started crawling and tried to get up, you fell over and over, but your parents were there to lift you and teach you.

It is teaching based on experience, in which the machine must deal with what went wrong before and look for the right approach.

Although we dont describe the reward policy that is, the game rules we dont give the model any tips or advice on how to solve the game. It is up to the model to figure out how to execute the task to optimize the reward, beginning with random testing and sophisticated tactics.

By exploiting research power and multiple attempts, reinforcement learning is the most successful way to indicate computer imagination. Unlike humans, artificial intelligence will gain knowledge from thousands of side games. At the same time, a reinforcement learning algorithm runs on robust computer infrastructure.

An example of reinforced learning is the recommendation on Youtube, for example. After watching a video, the platform will show you similar titles that you believe you will like. However, suppose you start watching the recommendation and do not finish it. In that case, the machine understands that the recommendation would not be a good one and will try another approach next time.

[Read: What audience intelligence data tells us about the 2020 US presidential election]

Reinforcement learnings key challenge is to plan the simulation environment, which relies heavily on the task to be performed. When trained in Chess, Go, or Atari games, the simulation environment preparation is relatively easy. Building a model capable of driving an autonomous car is key to creating a realistic prototype before letting the car ride the street. The model must decide how to break or prevent a collision in a safe environment. Transferring the model from the training setting to the real world becomes problematic.

Scaling and modifying the agents neural network is another problem. There is no way to connect with the network except by incentives and penalties. This may lead to disastrous forgetfulness, where gaining new information causes some of the old knowledge to be removed from the network. In other words, we must keep learning in the agents memory.

Another difficulty is reaching a great location that is, the agent executes the mission as it is, but not in the ideal or required manner. A hopper jumping like a kangaroo instead of doing what is expected of him is a perfect example. Finally, some agents can maximize the prize without completing their mission.


RL is so well known today because it is the conventional algorithm used to solve different games and sometimes achieve superhuman performance.

The most famous must be AlphaGo and AlphaGo Zero. AlphaGo, trained with countless human games, has achieved superhuman performance using the Monte Carlo tree value research and value network (MCTS) in its policy network. However, the researchers tried a purer approach to RL training it from scratch. The researchers left the new agent, AlphaGo Zero, to play alone and finally defeat AlphaGo 1000.

Personalized recommendations

The work of news recommendations has always faced several challenges, including the dynamics of rapidly changing news, users who tire easily, and the Click Rate that cannot reflect the user retention rate. Guanjie et al. applied RL to the news recommendation system in a document entitled DRN: A Deep Reinforcement Learning Framework for News Recommendation to tackle problems.

In practice, they built four categories of resources, namely: A) user resources, B) context resources such as environment state resources, C) user news resources, and D) news resources such as action resources. The four resources were inserted into the Deep Q-Network (DQN) to calculate the Q value. A news list was chosen to recommend based on the Q value, and the users click on the news was part of the reward the RL agent received.

The authors also employed other techniques to solve other challenging problems, including memory repetition, survival models, Dueling Bandit Gradient Descent, and so on.

Resource management in computer clusters

Designing algorithms to allocate limited resources to different tasks is challenging and requires human-generated heuristics.

The article Resource management with deep reinforcement learning explains how to use RL to automatically learn how to allocate and schedule computer resources for jobs on hold to minimize the average job (task) slowdown.

The state-space was formulated as the current resource allocation and the resource profile of jobs. For the action space, they used a trick to allow the agent to choose more than one action at each stage of time. The reward was the sum of (-1 / job duration) across all jobs in the system. Then they combined the REINFORCE algorithm and the baseline value to calculate the policy gradients and find the best policy parameters that provide the probability distribution of the actions to minimize the objective.

Traffic light control

In the article Multi-agent system based on reinforcement learning to control network traffic signals, the researchers tried to design a traffic light controller to solve the congestion problem. Tested only in a simulated environment, their methods showed results superior to traditional methods and shed light on multi-agent RLs possible uses in traffic systems design.

Five agents were placed in the five intersections traffic network, with an RL agent at the central intersection to control traffic signaling. The state was defined as an eight-dimensional vector, with each element representing the relative traffic flow of each lane. Eight options were available to the agent, each representing a combination of phases, and the reward function was defined as a reduction in delay compared to the previous step. The authors used DQN to learn the Q value of {state, action} pairs.


There is an incredible job in the application of RL in robotics. We recommend reading this paper with the result of RL research in robotics. In this other work, the researchers trained a robot to learn policies to map raw video images to the robots actions. The RGB images were fed into a CNN, and the outputs were the engine torques. The RL component was policy research guided to generate training data from its state distribution.

Web systems configuration

There are more than 100 configurable parameters in a Web System, and the process of adjusting the parameters requires a qualified operator and several tracking and error tests.

The article A learning approach by reinforcing the self-configuration of the online Web system showed the first attempt in the domain on how to autonomously reconfigure parameters in multi-layered web systems in dynamic VM-based environments.

The reconfiguration process can be formulated as a finite MDP. The state-space was the system configuration; the action space was {increase, decrease, maintain} for each parameter. The reward was defined as the difference between the intended response time and the measured response time. The authors used the Q-learning algorithm to perform the task.

Although the authors used some other technique, such as policy initialization, to remedy the large state space and the computational complexity of the problem, instead of the potential combinations of RL and neural network, it is believed that the pioneering work prepared the way for future research in this area


RL can also be applied to optimize chemical reactions. Researchers have shown that their model has outdone a state-of-the-art algorithm and generalized to different underlying mechanisms in the article Optimizing chemical reactions with deep reinforcement learning.

Combined with LSTM to model the policy function, agent RL optimized the chemical reaction with the Markov decision process (MDP) characterized by {S, A, P, R}, where S was the set of experimental conditions ( such as temperature, pH, etc.), A was the set of all possible actions that can change the experimental conditions, P was the probability of transition from the current condition of the experiment to the next condition and R was the reward that is a function of the state.

The application is excellent for demonstrating how RL can reduce time and trial and error work in a relatively stable environment.

Auctions and advertising

Researchers at Alibaba Group published the article Real-time auctions with multi-agent reinforcement learning in display advertising. They stated that their cluster-based distributed multi-agent solution (DCMAB) has achieved promising results and, therefore, plans to test the Taobao platforms life.

Generally speaking, the Taobao ad platform is a place for marketers to bid to show ads to customers. This can be a problem for many agents because traders bid against each other, and their actions are interrelated. In the article, merchants and customers were grouped into different groups to reduce computational complexity. The agents state-space indicated the agents cost-revenue status, the action space was the (continuous) bid, and the reward was the customer clusters revenue.

Deep learning

More and more attempts to combine RL and other deep learning architectures can be seen recently and have shown impressive results.

One of RLs most influential jobs is Deepminds pioneering work to combine CNN with RL. In doing so, the agent can see the environment through high-dimensional sensors and then learn to interact with it.

CNN with RL are other combinations used by people to try new ideas. RNN is a type of neural network that has memories. When combined with RL, RNN offers agents the ability to memorize things. For example, they combined LSTM with RL to create a deep recurring Q network (DRQN) for playing Atari 2600 games. They also usedLSTM with RL to solve problems in optimizing chemical reactions.

Deepmind showed how to use generative models and RL to generate programs. In the model, the adversely trained agent used the signal as a reward for improving actions, rather than propagating gradients to the entry space as in GAN training. Incredible, isnt it?

Reinforcement is done with rewards according to the decisions made; it is possible to learn continuously from interactions with the environment at all times. With each correct action, we will have positive rewards and penalties for incorrect decisions. In the industry, this type of learning can help optimize processes, simulations, monitoring, maintenance, and the control of autonomous systems.

Some criteria can be used in deciding where to use reinforcement learning:

In addition to industry, reinforcement learning is used in various fields such as education, health, finance, image, and text recognition.

This article was written by Jair Ribeiro and was originally published on Towards Data Science. You can read it here.

Published October 27, 2020 10:49 UTC

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Investing in Artificial Intelligence (AI) – Everything You Need to Know –

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Artificial Intelligence (AI) is a field that requires no introduction. AI has ridden the tailcoats of Moores Law which states that the speed and capability of computers can be expected to double every two years. Since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a doubling every 3 to 4 months, with the end result that the amount of computing resources allocated to AI has grown by 300,000x since 2012. No other industry can compare with these growth statistics.

We will explore what fields of AI are leading this acceleration, what companies are best positioned to take advantage of this growth, and why it matters.

Machine learning is a subfield of AI which is essentially programming machines to learn. There are multiple types of machine learning algorithms, the most popular by far is deep learning, this involves feeding data into an Artificial Neural Network (ANN). An ANN is a very compute intensive network of mathematical functions joined together in a format inspired by the neural networks found in the human brain.

The more big data that is fed into an ANN, the more precise the ANN becomes. For example, if you are attempting to train an ANN to learn how to identify cat pictures, if you feed the network 1000 cat pictures the network will have a small level of accuracy of perhaps 70%, if you increase it to 10000 pictures, the level of accuracy may increase to 80%, if you increase it by 100000 pictures, then you have just increased the accuracy of the network to 90%, and onwards.

Herein lies one of the opportunities, companies that dominate the field of AI chip development are naturally ripe for growth.

There are many other types of machine learning that show promise, such as reinforcement learning, this is training an agent through the repetition of actions and associated rewards. By using reinforcement learning an AI system can compete against itself with the intention of improving how well it performs. For example, a program playing chess will play against itself repeatedly, with every instance of the gameplay improving how it performs in the next game.

Currently the best types of AI use a combination of both deep learning and reinforcement learning in what is commonly referred to as deep reinforcement learning. All of the leading AI companies in the world such as Tesla use some type of deep reinforcement learning.

While there are other types of important machine learning systems that are currently being advanced such as meta-learning, for the sake of simplicity deep learning and its more advanced cousin deep reinforcement learning are what investors should be most familiar with. The companies that are at the forefront of this technological advancement will be best positioned to take advantage of the huge exponential growth we are witnessing in AI.

If there is one differentiator between companies that will succeed, and become market leaders, and companies that will fail, it is big data. All types of machine learning are heavily reliant on data science, this is best described as a process of understanding the world from patterns in data. In this case the AI is learning from data, and the more data the more accurate the results. There are some exceptions to this rule due to what is called overfitting, but this is a concern that AI developers are aware of and take precautions to compensate for.

The importance of big data is why companies such as Tesla have a clear market advantage when it comes to autonomous vehicle technology. Every single Tesla that is in motion and using auto-pilot is feeding data into the cloud. This enables Tesla to use deep reinforcement learning, and other algorithm tweaks in order to improve the overall autonomous vehicle system.

This is also why companies such as Google will be so difficult for challengers to dethrone. Every day that goes by is a day that Google collects data from its myriad of products and services, this includes search results, Google Adsense, Android mobile device, the Chrome web browser, and even the Nest thermostat. Google is drowning is more data than any other company in the world. This is not even counting all of the moonshots they are involved in.

By understanding why deep learning and data science matters, we can ten infer why the companies below are so powerful.

There are three current market leaders that are going to be very difficult to challenge.

Alphabet Inc is the umbrella company for all Google products which includes the Google search engine. A short history lesson is necessary to explain why they are such a market leader in AI. In 2010, a British company DeepMind was launched with the goal of applying various machine learning techniques towards building general-purpose learning algorithms.

In 2013, DeepMind took the world by storm with various accomplishments including becoming world champion at seven Atari games by using deep reinforcement learning.

In 2014, Google acquired DeepMind for $500 Million, shortly thereafter in 2015 DeepMinds AlphaGo became the first AI program to defeat a professional human Go player, and the first program to defeat a Go world champion. For those who are unfamiliar Go is considered by many to be the most challenging game in existence.

DeepMind is currently considered a market leader in deep reinforcement learning, and Artificial General Intelligence (AGI), a futuristic type of AI with the goal of eventually achieving or surpassing human level intelligence.

We still need to factor in the other other types of AI that Google is currently involved in such as Waymo, a market leader in automonous vehicle technology, second only to Tesla, and the secretive AI systems currently used in the Google search engine.

Google is currently involved in so many levels of AI, that it would take an exhaustive paper to cover them all.

As previously stated Tesla is taking advantage of big data from its fleet of on-road vehicles to collect data from its auto-pilot. The more data that is collected the more it can improve using deep reinforcement, this is especially important for what are deemed as edge cases, this is known as scenarios that dont happen frequently in real-life.

For example, it is impossible to predict and program in every type of scenario that may happen on the road, such as a suitcase rolling into traffic, or a plane falling from the sky. In this case there is very little specific data, and the system needs to associate data from many different scenarios. This is another advantage of having a huge amount of data, while it may be the first time a Tesla in Houston encounters a scenario, it is possible that a Tesla in Dubai may have encountered something similar.

Tesla is also a market leader in battery technology, and in electric technology for vehicles. Both of these rely on AI systems to optimize the range of a vehicle before a recharge is required. Tesla is known for its frequent on-air updates with AI optimizations that improve by a few percentage points the performance and range of its vehicle fleet.

As if this was not sufficient, Tesla is also designing its own AI chips, this means it is no longer reliant on third-party chips, and they can optimize chips to work with their full self-driving software from the ground up.

NVIDIA is the company best positioned to take advantage of the current rise in demand in GPU (Graphics processing unit) chips, as they are currently responsible for 80% of all GPUsales.

While GPUs were initially used for video games, they were quickly adopted by the AI industry specifically for deep learning. The reason GPUs are so important is that the speed of AI computations is greatly enhanced when computations are carried out in parallel. While training a deep learning ANN, inputs are required and this depends heavily on matrix multiplications, where parallelism is important.

NVIDIA is constantly releasing new AI chips that are optimized for different use cases and requirements of AI researchers. It is this constant pressure to innovate that is maintaining NVIDIA as a market leader.

It is impossible to list all of the companies that are involved in some form of AI, what is important is understanding the machine learning technologies that are responsible for most of the innovation and growth that the industry has witnessed. We have highlighted 3 market leaders, many more will come along. To keep abreast of AI, you should stay current with AI news, avoid AI hype, and understand that this field is constantly evolving.

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What if we wake up one morning to the news that a super-power AI has emerged with disastrous consequences? Nick Bostroms Superintelligent and Max Tegmarks Life 3.0 books argue that malevolent superintelligence is an existential risk for humanity.

Rather than endless anticipation, its better to ask a more concrete, empirical question: What would alarm us that superintelligence is indeed at the doorstep?

If an AI program develops fundamental new capabilities, thats the equivalent of a canary collapsing.

AIs performance in games like Go, poker, or Quake 3, is not a canary. The bulk of AI in such games is social work to highlight the problem and design the solution. The credit for AlphaGos victory over human Go champions was the talented human team at DeepMind that merely ran the algorithm the people had created. It explains why it takes several years of hard work to translate AI success from one little challenge to the next. Techniques such as deep learning are general, but their impactful application to a particular task needs extensive human intervention.

Over the past decades, AIs core success is machine learning, yet the term machine learning is a misnomer. Machines own only a narrow silver of humans versatile learning abilities. If you say machine learning is like baby penguins, know how to fish. The reality is that adult penguins swim, catch fish, digest it. They regurgitate fish into their beaks and place morsels into their childrens mouths. Similarly, human scientists and engineers are spoon-feeding AI.

In contrast to machine learning, human learning plans personal motivation to a strategic learning plan. For example, I want to drive to be independent of my parents (Personal motivation) to take drivers ed and practice on weekends (strategic learning). An individual formulates specific learning targets, collects, and labels data. Machines cannot even remotely replicate any of these human abilities. Machines can perform like superhuman; including statistical calculations, but that is merely the last mile of learning.

The automated formula of learning problems is our first canary, and it does not seem anywhere close to dying.

The second canary is self-driving cars. As Elon Musk speculated, these are the future. Artificial intelligence can fail catastrophically in atypical circumstances, like when an individual in a wheelchair crosses the street. In this case, driving is more challenging than any other AI task because it requires making life-critical, real-time decisions based on the unpredictable physical world and interaction with pedestrians, human drivers, and others. We should deploy a limited number of self-driving cars when they reduce accident rates. Human-level driving is achieved only when this canary be said to have kneeled over.

Artificial intelligence doctors are the third canary. AI already has the capability of analysing medical images with superhuman accuracy, which is a little slice of a human doctors job. An AI doctors responsibility would be interviewing patients, considering complications, consulting other doctors, and so on. These are challenging tasks, which require understanding people, language, and medicine. This type of doctor would not have to fool a patient into wondering it is human. Thats why it is different from the Turing test. A human doctor can do a wide range of tasks in unanticipated situations.

One of the worlds most prominent AI experts, Andrew Ng, has stated, Worrying about AI turning evil is a little bit like worrying about overpopulation on Mars.


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Monish Ramadoss, a computer science & engineering major with a keen interest in artificial intelligence, recalls hearing about the then-new AI@UCI Club in spring 2017. That first meeting, held in a windowless basement room of the Donald Bren School of Information & Computer Sciences, attracted a dozen or so like-minded undergraduates who wanted to discuss machine learning, computer vision and other aspects of AI.

Ramadoss kept attending the meetings for the new friendships he made as well as the stimulating subjects covered.

The idea of the club was to create an open forum for everybody to communicate their ideas about machine learning and other different topics an area where people could come together and develop new projects or even just connect, says Ramadoss, now a senior and one of 15 leaders of the AI@UCI Club.

The student-run organization currently has a mailing list of around 2,000 members.

During the academic year, 100 or so students gather twice a month for free workshops in which they get hands-on experience designing such things as chatbots think Alexa and other services that simulate conversations with humans by leveraging AI and natural language processing and fake online dating profiles, to illustrate the concept of generative adversarial networks (see definitions below). Once a quarter, guests speak at AI@UCI Club meetings about real-world applications of artificial intelligence.

Anthony Luu, a computer science major who, along with Ramadoss, is a group leader (they call themselves mentors), is interested in an AI-related career in the medical or cybersecurity field. At one club seminar, he talked about an app hes developing for UCI Dining Services: UCI is focused on zero waste. Im working on an app that allows users to take a picture of their trash, and then the app will tell them what bin to throw it in, along with instructions on how to properly dispose of it.

Club President Iman Amy Elsayed, a fifth-year computer science & engineering major, works closely with the groups academic adviser, Alex Ihler, a professor of computer science who teaches many of UCIs undergraduate machine learning classes.

AI encompasses so many different fields, Elsayed says, but at the end of the day, its all math. And I really enjoy math.

Says Ihler: The students approached me. The undergraduates wanted to have a club so they could get together and learn about different projects. A few years ago, AI clubs at universities were somewhat unusual. One reason is that machine learning in the past has not been very accessible to undergraduates. Now its starting to become more accessible and more relevant.

Shivan Vipani, a junior majoring in computer science, joined the AI@UCI Club when he was a freshman. I was interested in the workshops, and I wanted a little head start on being introduced to the world of AI, Vipani says. I thought itd be a cool way to learn and get my hands dirty.

To Andrew Laird, another club member and computer science major, AI feels like magic. He adds: Its super impressive what people have done with AI on the internet. When you get down to it, its all math and such, but its nice to be the magician.

With AI terminology a veritable alphabet soup of head-scratchers for the uninitiated, UCI Magazine asked mentors of the AI@UCI Club to explain in their own words what some of the key phrases mean.

Iman Amy Elsayed Fifth-year senior, computer science & engineering Career plans: computer vision/robotics engineer Mantra: Oreos are life.

Artificial Intelligence

Artificial intelligence is a broad term that means for a machine to demonstrate intelligence similar to humans or animals. The field encompasses several subfields such as computer vision, machine learning and natural language processing. While AI in the media often depicts a doomsday scenario, such as in The Terminator, machines dont have their own conscience and really only excel at what the programmer tells them to do. I like to describe programs as really smart 5-year-olds. They do exactly what you tell them to do.

Computer Vision

(object recognition and visual understanding) Face ID on iPhones is a popular application of computer vision, which allows technology to make sense of images, such as recognizing objects. When you open your iPhone, computer vision allows the phone to see you and unlock the phone once it recognizes you.

Jason Kahn Fifth-year senior, computer science and business information management Career plans: software and development and operations engineer Mantra: There is a time and place for decaf: never and in the trash.

Evolutionary Computation

(genetic algorithms, genetic programming) Think of this as a computerized version of Darwinism. Evolutionary computation refers to a machine learning method of optimization and learning inspired by genetics and evolution. Starting with a large group of possible solutions, we take the characteristics of the best-performing ones and produce a new set to eventually end up with the fittest. Its much like finding the best recipe for homemade cake: You try multiple different methods and find which aspects of each attempt produce the best-tasting cake. On each subsequent attempt, you only use methods that youve found create the best flavor. Over time, you end up with the perfect cake, making all your friends crown you the Cake Master.

Omkar Pathak Senior, computer science & engineering Career plans: machine learning software engineer Mantra: Enjoy what you do and do what you enjoy.

Speech Processing

(speech recognition and production) Virtual assistant technologies like Alexa, Siri or Google Assistant are great examples of speech processing AIs. When a user says Hey Siri, the iPhone employs speech recognition to understand what the user said. Then the AIs response is converted back to sound using speech synthesis, giving a more natural way to interact with your phone.

Shivan Vipani Junior, computer science Career plans: machine learning engineer Mantra: If you arent happy doing it, is it worth it?

Natural Language Processing

(machine translation) Natural language processing is the intersection between computer science and linguistics, dealing with how computers process and analyze human language. When your phone guesses what youll type next or Google answers a question for you, thats NLP hard at work to understand your sentence structure and the inherent meaning behind it. NLP is how systems like chatbots and Google Translate are able to run. As the volume of text information increases over time, NLP will play a key role in making sense of it all.

Explainable AI

Currently, AI is a black box where we dont always understand how an AI program makes a decision or comes to a conclusion. Explainable AI tries to extract and communicate why the AI program makes its decision in a way that humans can understand. Amy Elsayed

Andrew Laird Senior, computer science Career plans: machine learning software engineer Mantra: Work hard, play hard.

Reinforcement Learning

(scheduling, game playing) Reinforcement learning is the process of learning by interacting with an environment. Games are excellent examples of reinforcement learning. In 2016, Google DeepMinds AI, AlphaGo, beat the human world champion of Go an ancient Japanese game significantly more complex than chess. However, RL techniques are being applied to more than just games. Researchers are using RL to control robotic systems, optimize business strategies and even predict protein folding, which helps biologists fighting diseases, including COVID-19.

Machine Learning

Machine learning is a subfield of AI that uses statistical methods to make AI perform better with experience, or examples. We can teach a machine to recognize puppies by showing it many images of puppies and non-puppies (an example of supervised learning). The more examples of puppies we feed the machine to learn on, the better it will be at recognizing a puppy in a new image. Amy Elsayed

Anthony Luu Senior, computer science Career plans: use machine learning in the medical or cybersecurity field Mantra: Learn something new every day.

Supervised Learning

Most of the machine learning that you hear people talk about is supervised machine learning. Supervised learning uses labeled data and maps it between some observable information, or features (x) and output (y). This mapping allows the algorithm to take in something its never seen before and apply the mapping it learned from the data to produce a prediction of the correct output.

Satyam Sam Tandon Fifth-year senior, informatics, with a minor in statistics Career plans: data scientist/machine learning engineer Mantra: There are those who think pineapple goes on pizza and then there are those who are wrong.

Data Mining

Data mining is pretty great, despite its bad rap in popular media. (Remember how Target got slammed for being able to deduce, based on her purchases, that a teenage shopper was pregnant before her father knew it?) Instead of mining the Earth, data mining operates on large raw datasets, and instead of pickaxes, it uses tools like machine learning and statistics. In both, the goal is to chip away the surface and uncover hidden value in this case, patterns, trends and information. Such information can then be used for tasks ranging from providing better music recommendations to more accurately detecting cancer in X-rays.

Monish Ramadoss Senior, computer science & engineering Career plans: kernel engineer for AI accelerators Mantra: Live life and drink coffee.

Generative Adversarial Networks

Think of the classic cops and robbers scenario. In games, AI agents can use self-play to improve their skills. Generative adversarial networks are a similar idea used for supervised learning. We build two neural networks: a generator to make fake outputs and a discriminator to evaluate how real they are. Together they make a cops and robbers relationship in which the generator tries to fool the discriminator by creating more realistic outputs and the discriminator searches for ways to tell the difference. An example of this application is image upscaling, where a generator is trained to take a low-resolution image and upscale it to a higher resolution; the discriminator makes sure that the results look realistic.

Anurag Sengupta First-year graduate student, computer science Career plans: building software for machine learning applications Mantra: Love for sweets for any mood I happen to be in.

Recurrent Neural Networks

Recurrent neural networks are robust learning models that have found their application in various complex systems. Unmanned vehicles and robots defying gravity opened avenues in research in science and technology. Similarly, businesses benefit a lot from these methodologies in helping them analyze their data and everyday activities better. RNN serves as the state-of-the-art approach for determining future states of objects and data based on objects that happened earlier. Google Translate, Google Finance and Amazons Alexa chatbot all use such intuitive tools at the heart of their software.

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Do you knowPythonis named anall-rounder programming language? Sure, its,though it shouldnt be used on every single project, You should utilize it to create desktop purposes, video games, cellular apps, web sites, and system software program. Its even probably the most appropriate language for the implementation of synthetic intelligence and machine studyingalgorithms.

So, I spent the previous couple of weeks gatheringdistinctive mission concepts for anyPython developer. These mission concepts will hopefully convey again your curiosity on this wonderful language. The very best half is youll be able to improve your Python programming expertise with these enjoyable however difficult tasks.

Lets take a look at them one-by-one:

Today, large progress has been made within the discipline of desktop software improvement. You will note many drag & drop GUI builders and speech recognition libraries. So, why not be a part of them collectively and create a consumer interface by speaking with the pc?

That is purely a brand new idea and after some analysis, I discovered that nobody has ever tried to do it. So, it may be somewhat bit more difficult than those talked about beneath.

Listed below are some directions to get began on this mission utilizing Python. To begin with, you want these packages:-

Now, the thought is to hardcode some speech instructions like:

Since that is going to be aMinimal Viable Product (MVP), itsfully superb if its important to hardcode many conditional statements. You get the purpose, proper? Its quite simple and easy so as to add extra instructions like these.

After establishing some primary instructions, its time to check the code. For now, youll be able to attempt to construct a really primary login type in a window.

The foremost flexibility of this concept is it may be carried out for recreation improvement, web sites, and cellular apps. Even in several programming languages.

Betting is an exercise the place individuals predict an end result and in the event that theyre proper then they obtain a reward in return. Easy, proper? Now, there are various technological advances that occurred in synthetic intelligence or machine studying up to now few years.

For instance, you might need heard about applications likeAlphaGo Master,AlphaGo Zero, andAlphaZerothat may playGo (game)higher than any skilled human participant. Youll be able to even get thesource codeof the same program referred to as Leela Zero.

The purpose I need to convey is that AI is getting smarter than us. Which means it may predict one thing higher by making an allowance for all the chances and study from previous experiences.

Lets apply some supervised studying ideas in Python to create an AI Betting Bot. Listed below are some libraries it is advisable get began:

To start, it is advisable choose a recreation (e.g. tennis, soccer, and many others.) for predicting the outcomes. Now,seek for historic match outcomes information that can be utilized to coach the mannequin.

For instance, the info of tennis matches might be downloaded in .csv format from website.

In case youre not aware of betting, right heres the way it works.

After coaching the mannequin, now we have to compute theConfidence Degreefor every prediction, discover out the efficiency of our bot by checking what number of instances the prediction was proper, and at last keep watch overReturn On Funding (ROI).

Obtain the same open-sourceAI Betting Bot Projectby Edouard Thomas.

A buying and selling bot is similar to the earlier mission as a result of it additionally requires AI for prediction. Now the query is whether or not an AI can appropriately predict the fluctuation of inventory costs? And, the reply is Sure.

Earlier than getting began, we want some information to develop a buying and selling bot:

These assets from Investopedia may assist in coaching the bot:1

After studying each of those articles, youll now have a greater understanding of when to purchase shares and when to not. This data can simply be remodeled right into a Python program that mechanically makes the choice for us.

You can even take reference from this open-source buying and selling bot referred to asfreqtrade. Its constructed utilizing Python and implements a number of machine studying algorithms.

This concept is taken from the Hollywood film sequenceIron Man. The film revolves round know-how, robots, and AI.

Right here, the Iron Man has constructed a digital assistant for himself utilizing synthetic intelligence. This system is namedJarvisthat helps Iron Man in on a regular basis duties.

Iron Man provides directions to Jarvis utilizing easy English language and Jarvis responds in English too. It implies that our program will want speech recognition in addition to text-to-speech functionalities.

Id suggest utilizing these libraries:

For now, youll be able to hardcode the speech instructions like:

When you set analarm on cellular, you may as well use Jarvis for tons of different duties like:

Even Mark Zuckerberg has constructed aJarvisas a side-project.

Songkickis a extremely popular service that gives details about upcoming live shows. ItsAPIcan be utilized to seek for upcoming live shows by:

Youll be able to create a Python script that retains checking a selected live performance day by day utilizing Songkicks API. With all this arrange, youll be able to ship an e mail to your self each time the live performance is obtainable.

Generally Songkick even shows apurchase ticketshyperlink on their web site. However, this hyperlink may go to a special web site for various live shows. It means its very tough to mechanically buy tickets even when we make use of internet scraping.

As a substitute, we will merely show the purchase tickets hyperlink because its in our software for handbook motion.

Lets Encryptis a certificates authority that provides free SSL certificates. However, the difficulty is that this certificates is barely legitimate for 90 days. After 90 days, its important to renew it.

In my view, this can be a nice situation for automation utilizing Python. We will write some code that mechanically renews an internet site SSL certificates earlier than expiring.

Take a look at thiscode on GitHubfor inspiration.

Today, governments have put in surveillance cameras in public locations to extend the safety of their residents. Most of those cameras are merely to report video after which the forensic consultants need to manually acknowledge or hint the person.

What if we create a Python program that acknowledges every particular person in digital camera in real-time. To begin with, we want entry to a nationwide ID card database, which we in all probability dont have, clearly.

So, a simple choice is to create a database with your loved ones members information.

Youll be able to then use aFace Recognitionlibrary and join it with the output of the digital camera.

Contact Tracing is a method to establish all those who have come into contact with one another throughout a selected time interval. Its principally helpful in a pandemic like COVID-19 as a result of with none information about whos contaminated, we willt cease its unfold.

Python can be utilized with a machine studying algorithm referred to asDBSCAN (Density-Based mostly Spatial Clustering of Purposes with Noise)for contact tracing.

As that is only a side-project, so we dont have entry to any official information. For now, its higher to generate some reasonable check information utilizingMockaroo.

You will have a take a look atthis articlefor particular code implementation.

Nautilus File Supervisor in Ubuntu

This can be a very primary Python program that retains monitoring a folder. At any time when a file is added in that folder it checks its sort and strikes it to a selected folder accordingly.

For instance, we will observe our downloads folder. Now, when a brand new file is downloaded, then it should mechanically be moved in one other folder in line with its sort.

.exe information are likely software program setups, so transfer them contained in the software program folder. Whereas, shifting photographs (png, jpg, gif) contained in the photographs folder.

This manner we will arrange several types of information for fast entry.

Create an software that accepts the names of expertise that we have to study for a profession.

For instance, to develop into an internet developer, we have to study:

After coming into the talents, there shall be aGenerate Profession Pathbutton. It instructs our program to go lookingYouTubeand choose related movies/playlists in line with every ability. In case there are various related movies for ability then it should choose the one with probably the most views, feedback and likes.

This system then teams these movies in line with expertise and show their thumbnail, title, and hyperlink within the GUI. It is going to additionally analyze the length of every video, combination them, after which inform us about how a lot time it should take to study this profession path. Now, as a consumer, we will watch these movies that are ordered in a step-by-step method to develop into a grasp on this profession.

Difficult your self with distinctive programming tasks retains you energetic, improve your expertise, and helps you discover new prospects. A few of the mission concepts I discussed above can be used as yourUltimate Yr Venture. Its time to indicate your creativity with Python programming language and switch these concepts into one thing youre pleased with.

This article was initially printed on Live Code Stream by Juan Cruz Martinez (twitter: @bajcmartinez), founder and writer of Dwell Code Stream, entrepreneur, developer, writer, speaker, and doer of issues.

Live Code Stream can also be accessible as a free weekly publication. Join updates on every thing associated to programming, AI, and pc science basically.

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Test your Python skills with these 10 projects - Best gaming pro

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October 3rd, 2020 at 5:57 am

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