Category Archives: Artificial Intelligence

The future of baby-making is set to be very different from the one we have now. Just last week, a researcher boldly claimed that growing embryos in a laboratory setting will become far more commonplace, and will allow us to remove genetic diseases from the equation before the baby is born.

Now, during the annual meeting of the European Society of Human Reproduction and Embryology in Geneva, scientists have given us yet another peek into the future of conception. In a groundbreaking new study, a team of embryologists was pitted against an artificial intelligence (AI) during simulated in vitro fertilization (IVF) selection process and the AI appeared to be better at selecting viable embryos.

During IVF, an egg is removed from the hopeful mothers ovaries and fertilized with the potential fathers sperm in a laboratory setting. This fertilized egg is then implanted in the womans womb and allowed to develop normally.

Its used for those with fertility problems, and currently has variable rates of success. Sometimes, the embryos fail for a variety of reasons, and experts are trained to look out for defects that may trigger a failed pregnancy. Between 30 to 60 percent of seemingly viable embryos fail to implant in the uterus.

This new study a collaborative effort between So Paulo State University and Londons Boston Place Clinic decided to pit experts against an AI designed to do their jobs for them. Using bovine embryos, the AI was given a chance to train itself to look for viable embryos and highlight defective ones.

Both the AI and a team of embryologists were then given 48 examples of bovine embryos to look at, and had a chance to observe them three times over.

Using just 24 key characteristics, such as morphology, texture, and the quantity and quality of the cells present, the AI was able to pick viable embryos 76 percent of the time. Although the accuracy value for the embryologists was not given, it was said to be lower; importantly, unlike the AI, the embryologists found it difficult getting a consensus on the quality of the embryos.

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Artificial Intelligence Better Than Medical Experts At Choosing Viable IVF Embryos - IFLScience

NASAs Frontier Development Lab (FDL), a public-private research institute operated jointly by the space agencys Ames Research Center and the SETI Institute, announced it will use artificial intelligence to study methods of protecting the Earth from potentially hazardous asteroids and comets.

The announcement was made on Friday, June 30, designated in 2014 as International Asteroid Day, an annual event that addresses potential threats from Near Earth Objects (NEOs).

June 30 was chosen because it is the anniversary of the 1908 Tunguska impact, when an asteroid estimated to have been 120 feet wide exploded over the Stony Tunguska River in Siberia.

The annual commemoration is the brainchild of astrophysicist and Queen lead guitarist Brian May and film director Grigorij Richters.

Several years ago, Richters directed 51 Degrees North, a film depicting a fictional asteroid strike in London.

For this years event, FDL assembled a research team to discuss the ways artificial intelligence can assist in planetary defense. In addition to addressing the issue of potentially hazardous asteroids and comets, the researchers also dealt with the possible threat from solar storms.

Now in its second year, FDL partners with various private and academic organizations, including Luxembourg Space Resources, Lockheed Martin, IBM, Intel, Nvidia, and various other corporations.

Using an interdisciplinary approach, FDL brings together machine learning with scholars in a diversity of fields, including planetary science and heliophysics.

Grand challenges like planetary defense require ingenious approaches, said FDL Director James Parr. We wanted to create a platform that industrializes breakthrough work useful to the space program and the task of protecting our planet.

Researchers at the conference discussed options such as using machine learning to model the orbits of long period comets, automating 2D research data into 3D images of asteroids to identify their spin rates and shapes, using data mining to further study space weather produced by interactions between the Sun and the Earth, utilizing machine learning to provide early warnings of solar storms, and merging machine learning and other data to search for water sources on the Moon.

Laurel Kornfeld is a freelance writer and amateur astronomer from Highland Park, NJ, who enjoys writing about astronomy and planetary science. She studied journalism at Douglass College, Rutgers University, and earned a Graduate Certificate of Science in astronomy from Swinburne Universitys Astronomy Online program.

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NASA will use artificial intelligence for planetary defense - The Space Reporter

Home computing artwork for the Artificial Intelligence compilation. Photograph: Warp Records

In the days of ever-changing playlists and unlimited Soundcloud mixes it might seem strange that something as simple as a compilation album could change the course of music. And yet that was what happened 25 years ago this month, in July 1992, with the release of Warp Records first Artificial Intelligence compilation. It was a record that helped to launch the careers of Autechre, Aphex Twin and Richie Hawtin, birthed the genre that would later become known as intelligent dance music (or IDM), and changed the idea of electronic music as merely a tool for dancing.

Artificial Intelligence wore its heart on its sleeve: the front cover features an android slumped in an armchair in front of a stereo, with albums from Kraftwerk and Pink Floyd scattered around. Below this, the tagline electronic listening music from Warp spelled out the compilations modus operandi: this was electronic music for the home, not the rave a notion that was largely foreign 25 years ago.

In retrospect, the compilations tracklisting was equally historic. Aphex Twin, whose classic Selected Ambient Works 85-92 album had been released just five months previously, contributed the eerie Polygon Window under the pseudonym The Dice Man; Autechre appeared twice, with the joyous electro of Crystel and the Egg; Richie Hawtin (as UP!) was responsible for Spiritual High, a pulsating acid track that feels a little out of place in its out-and-out embrace of the dancefloor; Warp stalwarts Black Dog Productions (as IAO) contributed the warm electronic embrace of The Clan; B12 (as Musicology) served up breakbeat techno on Telefone 529 and the bleep-inspired Preminition; and Dutch producer Speedy J gave us elegant breakbeat number De-Orbit (and Fill 3 on the CD release). Even the Orb contributed, under the guise of leader Dr Alex Paterson, closing the record with a gorgeous live take on A Huge Ever Growing Pulsating Brain That Rules from the Centre of the Ultraworld, known as Loving You Live.

The focus on electronic listening music that Artificial Intelligence encouraged may have been unusual but it was not entirely without precedent, even in 1992. The classic Detroit techno productions of the late 80s notably those of Derrick May had brought an increased melodic sophistication to dance music, while in the UK artists like the Orb and the KLF had helped to pioneer the armchair-friendly sound of ambient house. Meanwhile, Belgiums R&S Records probably Warps only real rival in terms of 1990s intelligent techno had already put out pioneering, thoughtful releases from the likes of Rising High Collective, Nexus 21 and Sun Electric.

You can hear these influences running through Artificial Intelligence. But Warp managed to codify this new strain of electronic music, signalling their intentions via the compilations name, strap line and cover art, as Warp co-founder Steve Beckett explained in Simon Reynolds Generation Ecstasy: You could sit down and listen to it like you would a Kraftwerk or Pink Floyd album. Thats why we put those sleeves on the cover of Artificial Intelligence to get it into peoples minds that you werent supposed to dance to it.

Warp would go on to release a groundbreaking series of electronic music albums under the Artificial Intelligence name (featuring all of the artists who appeared on the first AI comp apart from the Orb) leading to the release in May 1994 of the second, slightly disappointing compilation. By this time, though, the genre Warp had earmarked as electronic listening music and which had variously been known as art techno, intelligent techno and electronica had found itself another name, one that would prove hugely controversial over the years: IDM.

The new name had its origins in the electronic mailing list, then the bleeding edge of communication technology. In August 1993 the Hyperreal organisation set up the Intelligent Dance Music list to discuss music relating to Aphex Twin and Warps early Artificial Intelligence compilations (Aphex Twins Rephlex label also featured heavily). It was a name that proved controversial from the off, with its rather snobbish focus on intelligence being at odds with the all in it together ethos of rave (although, you could argue that such apparent snootiness was a precursor to the trainspotting Discogs nerdery that exists today). One of the very first posts to the new list asked can dumb people enjoy IDM, too? and few, if any, of the artists associated with the term have ever embraced it. And yet the name endured, particularly in the US where rave made less of an impact and electronic music was, for many years, an underground phenomenon that spread largely online.

The term IDM survives into 2017, although it remains as stubbornly hard to tie down as ever. If it was once defined by the Artificial Intelligence series, then the further we get from that series release, the harder it is to say who exactly is IDM among the fractured, ever-expanding array of electronic music sounds. Is Jlin, an artist who picked up comparison to the likes of Squarepusher thanks to her intricate post-footwork rhythmical mazes, IDM? How about Flying Lotus, who featured in Pitchforks recent 50 Best IDM Albums of All Time? Or Nina Kraviz and her label?

Well, until someone thinks of something better and stuff that sounds a bit like Aphex Twin just isnt going to cut it we might just be stuck with it. Either way, these kinds of taxonomic discussions are thankfully reserved for the most arid corners of the web, allowing Artificial Intelligences true legacy to shine: the album that announced techno as music for the mind as well as the feet.

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Machines of loving grace: how Artificial Intelligence helped techno grow up - The Guardian

Posted July 03, 2017 20:07:28

The fourth industrial revolution is underway and it's threatening to wipe out nearly half the jobs in Australia.

This latest round is characterised by intelligent robots and machine learning and PricewaterhouseCoopers economist Jeremy Thorpe said it's going to completely reshape the Australian jobs market.

"Over the next 20 years approximately 44 per cent of Australia's jobs, that's more than 5 million jobs, are at risk of being disrupted by technology, whether that's digitisation or automation," he said.

Stefan Hajkowicz, who is the principal scientist at the CSIRO, says it's white collar workers who are about to feel the pain.

"The sort of job losses that we did see in the manufacturing sector in Australia the car manufacturing sector are going to get into the administrative services and financial services sector in downtown CBD postcodes and that's the big challenge that lies in front of us," he said.

Mr Thorpe agrees, adding that white-collar workers in Australia were "the big growth sector over the last 30 years".

"We were the beneficiaries of globalisation and it's going to be a shock to the system when we see not just the growth temper, we actually see a decline in those sorts of jobs."

Australian financial start up Stockspot says its business model makes thousands of highly paid jobs obsolete.

It claims that by using algorithms and automation instead of people, they can provide better financial advice at a cheaper price.

Founder Chris Brycki said some jobs, particularly in the financial services sector, don't add value.

"Financial services employs about 10 per cent of our workforce and, really, a lot of those jobs are unnecessary," he said.

"A lot of research analysts, stock pickers, stockbrokers, they don't actually add any end value for the consumer."

Mr Hajkowicz said the technology behind digital currency bitcoin known as blockchain also threatens to seriously shake up the industry.

"Blockchain and distributive ledger technology, if it plays out the way we think it can, this is the technology that sits behind the bitcoin currency and can be used for smart invoicing or auditing processes," he said.

"It could turn the job of 100 auditors into one."

The job losses in finance have already begun, with Westpac reducing its headcount over the last year.

But the real hit is still to come.

A Macquarie analyst recently predicted the big four might look to shed 20,000 jobs over the coming years.

It's already happened overseas. In the decade following the great recession, the banking workforce in the US dropped by around half a million people.

Mr Brycki said we will feel the pain here soon.

"The reason we are behind the US and the UK is that we didn't go through the financial crisis as badly, and that flushed out a lot of people from the industry," he said.

But it was only a temporary reprieve.

"A lot of people are still in the stale jobs in banks and it's not until the banks have to lay people off in the next few years that the [financial] tech industry and this disruption will really flourish," he said.

It's not just start-ups threatening existing business models.

The big tech giants are also continually innovating and threatening to push further into the finance space.

"Apple may be better placed to be a bank, Google might be better placed to be a bank than an actual bank because it has technology to facilitate the transaction," Mr Brycki said

He says young people eyeing off what are currently lucrative careers option will be forced to reconsider.

"I came in to the industry at the very top it was around 2006 when I joined," he said.

"We'll probably never see that level of salaries and bonuses and the craziness in financial services because of the structural changes that are going to happen."

Mr Thorpe said the evidence is already building.

"It is the boiling frog syndrome that we are experiencing at the moment," he said.

"You may not realise that we're already seeing some jobs disappear, for some jobs are being restructured because of automation and digitisation."

This is part one of a three part special by The Business and Business PM which looks at on how automation will reshape the Australian workforce.

Topics: robots-and-artificial-intelligence, banking, business-economics-and-finance, industry, economic-trends, globalisation---economy, multinationals, science-and-technology, australia

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Rise of the machines: How artificial intelligence will reshape our lives - ABC Online

Complex computer software may be the key to correctly diagnosing and treating patients with various diseases.

Dr. Nick Ernest, a UC graduate who beat the Air Force in a simulated game of aerial combat with his artificial intelligence (AI) system, is now applying the concept to the human body.

In a proof of concept study, Ernest harnessed the power of his Psibernetix AI program to determine if bipolar patients could benefit from a certain medication. Using fMRIs of bipolar patients, the software looked at how each patient would react to lithium.

Fuzzy Logic appears to be very accurate

The computer software predicted with 100 percent accuracy how patients would respond. It also predicted the actual reduction in manic symptoms after the lithium treatment with 92 percent accuracy.

UC psychiatrist David Fleck partnered with Ernest and Dr. Kelly Cohen on the study. Fleck says without AI, coming up with a treatment plan is difficult. "Bipolar disorder is a very complex genetic disease. There are multiple genes and not only are there multiple genes, not all of which we understand and know how they work, there is interaction with the environment."

Ernest emphasizes the advanced software is more than a black box. It thinks in linguistic sentences. "So at the end of the day we can go in and ask the thing why did you make the prediction that you did? So it has high accuracy but also the benefit of explaining exactly why it makes the decision that it did."

More tests are needed to make sure the artificial intelligence continues to accurately predict medication for bipolar patients.

AI could work for other diseases

Ernest says there's no reason this wouldnt work for other illnesses.

It almost doesnt matter what the application is. This could have easily been whether this person responded well to a surgery or a different drug. With my company, we use this methodology with determining costs and markets, maintenance for machinery. Really any sort of predictive analytics or big learning type application could utilize this.

Ernest has started another study. Its to predict recovery rates for people who have had a concussion.

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Artificial Intelligence Key To Treating Illness - WVXU

Xconomy San Diego

This story is part of an ongoing Xconomy series on A.I. in healthcare.

These are heady times for using artificial intelligence to extract insights from healthcare datain particular, from the tidal wave of information coming out of fields like genomics and medical imaging.

Yet as innovations proliferate, some age-old business questions have come to the fore. How can startups make money in this emerging field? How can healthcare companies use AI to bend the curve of increasing healthcare costs? And, ultimately, how can they get buy-in from government regulators, insurers, doctors, and patients? These were some of the issues that emerged this spring when Xconomy brought together some of San Diegos most-prominent tech and life sciences leaders for a dinner discussion about the risks and opportunities in the convergence of AI and healthcare.

Being a healthcare investor, I love the fact that theres interest now on the tech side, said Kim Kamdar, a partner in the San Diego office of the venture firm Domain Associates. It opens up a whole new avenue of potential co-investors for our companies.

The consensus: Its still early days for applying machine learning and related techniques in healthcare, and its hard to foresee how these innovations will play out. As Xconomy senior editor Jeff Engel has reported, questions abound over the impact AI will have on doctors and healthcare institutions. Yet there is little doubt that transformational change is coming, and tech companies ranging in size from small startups to corporate titans like IBM and GE are scrambling to gain a foothold in this emerging field.

If ever there was a sector in need of transformational disruption, it would be healthcare, where spending in the United States amounts to more than $3.2 trillion a yearand accounts for close to 18 percent of the U.S. gross domestic product.

The sector represents a lucrative-but-daunting target for investorscomplicated by regulatory issues, a healthcare system that separates the interests of patients, providers, and payers, and an investment timeline that can take 10 years or more to realize.

There may be no better example of the potential opportunities than Grail, the $1 billion-plus startup spun out by Illumina (NASDAQ: ILMN) to advance diagnostic technology sensitive enough to detect fragments of cancer DNA in a routine blood sample. Yet cautionary tales also aboundmost notably with Theranos, the venture-backed diagnostic company that was valued at $9 billion in 2015and plunged last year to less than a tenth of that.

Interest in healthcare AI runs high in San Diego, which has a well-established life sciences cluster and is home to two genome sequencing giants: Illumina and the life sciences solutions group of Thermo Fisher Scientific (NYSE: TMO). San Diego also has some resident expertise in neural networking technologies that accompanied the rise of HNC Software, a developer of analytic software for the financial industry that is now used by FICO (NYSE: FICO) to predict credit card fraud, among other things. (FICO acquired HNC in 2002 in a stock deal valued at $810 million.)

The dinner conversation that Xconomy convened included Kamdar and other local investors, data scientists, healthcare CTOs, startup founders, academic researchers, and digital health executives. The kickoff question: Is there a proven business model for startups that are applying innovations in machine learning in the life sciences?

The model that came to mind for Next Page

Bruce V. Bigelow is the editor of Xconomy San Diego. You can e-mail him at bbigelow@xconomy.com or call (619) 669-8788

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What's the Business Model for Artificial Intelligence in Healthcare? - Xconomy

06:01 Monday 03 July 2017

Businesses and the government must ready the nations workforce for the rise of artificial intelligence to ensure companies can ride out the cliff edges created by the technological revolution, according to PwC.

The professional services firm said AI had the power to overhaul business models and could leave workers sidelined and companies struggling to adjust unless preparations are made now.

It said firms and the state must step up their efforts to improve the education system and help workers retrain to ensure AI delivers the much-heralded boost to the UK economy.

Jon Andrews, PwCs head of technology and investments, said: There are different sectors that will be impacted in different ways.

The vast majority [of workers] will not see the change happening to them and they will have a very different job by 2030. But some of them you can see coming and you can actually predict the changes.

If you take the logistics world, there is going to be a period of time as we move towards autonomous vehicles where it will continue to be cheaper to have an old vehicle that is non-autonomous with a person driving it.

Then all of sudden that will flip and the business case will change and it will be worthwhile making the investment in autonomous vehicles. So we will see a cliff in terms of jobs there going more quickly.

We need to be prepared as a country on how we re-train people to think what other jobs those people can do ahead of that.

But that will be largely predictable because you will be able to predict and seethat business case changing.

Experts believe the rise of AI poses a threat to workers across the professions, from staff in fast-food restaurants to journalists, accountants and doctors.

Around 30 per cent of UK jobs are at high risk of being eradicated by AI by 2030, PwC has estimated.

Billionaire Microsoft founder Bill Gates mooted the possibility of creating a robot tax in order to plug the hole in public finances left by the jobs destroyed by automation.

However, the rise of AI coined the fourth industrial revolution will also create new roles for human beings and could drive up productivity and bolster economic growth.

Jonathan Gillham, PwCs director of economics, said: We need to upskill workers that are currently in the labour market and improve our education systems.

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Workers 'must be trained to cope with rise of artificial intelligence' - The Scotsman

Machines might one day replace human laborers in a number of professions, but surely they won't ever replace human artists. Right?

Think again. Not even our artists will be safe from the inevitable machine takeover, if a new development in artificial intelligence by a team of researchers from Rutgers University and Facebooks A.I. lab offers an example of what's to come. They have designed an A.I. capable of not only producing art, but actually inventing whole new aesthetic styles akin to movements like impressionism or abstract expressionism, reports New Scientist.

The idea, according to researcher Marian Mazzone, who worked on the system, was to make art that is novel, but not too novel. It's such an effective system that the art produced by it is already being given the thumbs up by human critics when presented in public.

The algorithm at play is a modification of what is known as a generative adversarial network (GAN), which essentially involves two neural nets that play off against each other to get better and better results. The model used in this project involved a generator network, which produces the images, and a discriminator network, which "judges" whether it is art. The discriminator is programed with knowledge of 81,500 examples of human paintings that either count as art or don't, as well as knowledge of how to categorize art into known styles, and it uses these benchmarks to carry out the judging process.

This may seem overly simplistic, but there's a twist. Once the generator learns how to produce work that the distributor recognizes as art, it is given an additional directive: to produce art that doesn't match any known aesthetic styles.

You want to have something really creative and striking but at the same time not go too far and make something that isnt aesthetically pleasing, explained team member Ahmed Elgammal.

The art that was generated by the system was then presented to human judges alongside human-produced art without revealing which was which. To the researchers' surprise, the machine-made art was actually scored slightly higher overall than the human-produced art.

Of course, machines can't yet replace the meaning that's infused in works by human artists, but this project shows that artist skillsets certainly seem duplicatable by machines.

What will it take for machines to produce content that is infused with meaning? That might be the last A.I. frontier. Human artists can at least hang their hats on that domain... for now.

Imagine having people over for a dinner party and they ask, Who is that by? And you say, Well, its a machine actually. That would be an interesting conversation starter, said Kevin Walker, from the Royal College of Art in London.

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Artificial intelligence may soon replace our artists as well - Mother Nature Network

LONDON (Reuters) - The world's leading drug companies are turning to artificial intelligence to improve the hit-and-miss business of finding new medicines, with GlaxoSmithKline unveiling a new $43 million deal in the field on Sunday.

Other pharmaceutical giants including Merck & Co, Johnson & Johnson and Sanofi are also exploring the potential of artificial intelligence (AI) to help streamline the drug discovery process.

The aim is to harness modern supercomputers and machine learning systems to predict how molecules will behave and how likely they are to make a useful drug, thereby saving time and money on unnecessary tests.

AI systems already play a central role in other high-tech areas such as the development of driverless cars and facial recognition software.

"Many large pharma companies are starting to realise the potential of this approach and how it can help improve efficiencies," said Andrew Hopkins, chief executive of privately owned Exscientia, which announced the new tie-up with GSK.

Hopkins, who used to work at Pfizer, said Exscientia's AI system could deliver drug candidates in roughly one-quarter of the time and at one-quarter of the cost of traditional approaches.

The Scotland-based company, which also signed a deal with Sanofi in May, is one of a growing number of start-ups on both sides of the Atlantic that are applying AI to drug research. Others include U.S. firms Berg, Numerate, twoXAR and Atomwise, as well as Britain's BenevolentAI.

"In pharma's eyes these companies are essentially digital biotechs that they can strike partnerships with and which help feed the pipeline," said Nooman Haque, head of life sciences at Silicon Valley Bank in London.

"If this technology really proves itself, you may start to see M&A with pharma, and closer integration of these AI engines into pharma R&D."

It is not the first time drugmakers have turned to high-tech solutions to boost R&D productivity.

The introduction of "high throughput screening", using robots to rapidly test millions of compounds, generated mountains of leads in the early 2000s but notably failed to solve inefficiencies in the research process.

When it comes to AI, big pharma is treading cautiously, in the knowledge that the technology has yet to demonstrate it can successfully bring a new molecule from computer screen to lab to clinic and finally to market.

"It's still to be proven, but we definitely think we should do the experiment," said John Baldoni, GSK's head of platform technology and science.

Baldoni is also ramping up in-house AI investment at the drugmaker by hiring some unexpected staff with appropriate computing and data handling experience - including astrophysicists.

His goal is to reduce the time it takes from identifying a target for disease intervention to finding a molecule that acts against it from an average 5.5 years today to just one year in future.

"That is a stretch. But as we've learnt more about what modern supercomputers can do, we've gained more confidence," Baldoni told Reuters. "We have an obligation to reduce the cost of drugs and reduce the time it takes to get medicines to patients."

Earlier this year GSK also entered a collaboration with the U.S. Department of Energy and National Cancer Institute to accelerate pre-clinical drug development through use of advanced computational technologies.

The new deal with Exscientia will allow GSK to search for drug candidates for up to 10 disease-related targets. GSK will provide research funding and make payments of 33 million pounds ($43 million), if pre-clinical milestones are met.

($1 = 0.7682 pounds)

Reporting by Ben Hirschler; Editing by Adrian Croft/Keith Weir

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Big pharma turns to artificial intelligence to speed drug discovery, GSK signs deal - Reuters

Artificial Intelligence is a computer program of a higher order and nothing else.

When I saw men fighting off a sinister takeover attempt by machines in Terminator 2- The Judgment Day, 25 years ago, I laughed it off, even though I enjoyed the thrill of the movie.

Man versus machine is probably the second best bogey after God versus Lucifer eternal battle.

Of course, we all want the man to win. We cant imagine ourselves serving some metal bodies, after all. But there may be some among us who are still wondering if the consequences of AI would eventually lead us there.

Recently, a senior manager in analytics in one my client companies, a very large business house indeed, was infatuated with the idea that AI can eventually take over human intelligence. That was surprising because he is not a teenager looking for cheap excitement or someone who does not know what analytics is about.

In fact, he has a pedigree of working for one of the largest analytics companies in the world before he joined my client company. Until now, I thought this idea is for Hollywood filmmakers who are short on creativity. But I think it is better to put this into right perspective as folks are churning enormous hype about AI, confusing everyone as usual.

AI means different things to different people. Some visualise machines working for their own purposes like in Terminator movies. Others imagine something like Watson that is so intelligent that it has solutions to all kinds of problems of mankind. Yet, to some data scientists, it means a piece of python code or a software package which they can run every day to earn a living.

But we can broadly divide AI into two streams: Generalised AI, which we call as Machine Learning (ML) and Applied AI, which focuses on replicating human behavior, such as making robots.

In either of the cases, it is a computer program of a higher order and nothing else!

Let me explain. In programming, we define what a program has to do. We then input data and get an output. We look at the output and if its not satisfactory enough, we go and correct the program. Now, what if, the program itself can look at the output and improve for itself? That is MLor generalised AI. But how does it do that?

Suppose you want to guess the next product a customer is going to buy on Amazon or anywhere else based on her activity until now. If you are a predictive modeler from econometric school, you would want to look at all historical data and find out the factors that determine a customers behavior and use that learning to predict what this customer would do now in the near future.

In reality, these factors can be anything. It can be demographic factors such as her age, marital status, location, education, or occupation. Or it can be the offers of competing products available at that point in time. Or let us say, even the weather influencing her buying behavior, or just that she is frustrated with the results of the American presidential elections. And, lets not forget the influence of her boyfriend on her buying moods?

As we can see, the possibilities are many. And if we consider further possibilities of all the interactions of these different factors among themselves, which means each factor having a partial influence by itself and a combined influence along with some other factors, then the combinations become unmanageable to human attention.

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Artificial Intelligence versus humans, who will win? - YourStory.com