IBM AI

Monday 24th March 2014

The Singularity

The advances we’ve seen in the past few years—cars that drive themselves, useful humanoid robots, speech recognition and synthesis systems, 3D printers, Jeopardy!-champion computers—are not the crowning achievements of the computer era. They’re the warm-up acts. As we move deeper into the second machine age we’ll see more and more such wonders, and they’ll become more and more impressive.

How can we be so sure? Because the exponential, digital, and recombinant powers of the second machine age have made it possible for humanity to create two of the most important one-time events in our history: the emergence of real, useful artificial intelligence (AI) and the connection of most of the people on the planet via a common digital network.

Either of these advances alone would fundamentally change our growth prospects. When combined, they’re more important than anything since the Industrial Revolution, which forever transformed how physical work was done.

We can’t predict exactly what new insights, products, and solutions will arrive in the coming years, but we are fully confident that they’ll be impressive. The second machine age will be characterized by countless instances of machine intelligence and billions of interconnected brains working together to better understand and improve our world. It will make mockery out of all that came before. Erik Brynjolfsson & Andrew McAfee

 

Monday 19th May 2014

5 Areas in Robotics that will Transform Society and Their Economic Impact

1- Drones: The next 5 years for drones is very promising. Expect to see drones becoming part of society’s information infrastructure as News agencies, TV companies, photographers, real estate agents, moviemakers, industrial giants, pizza deliveries, logistic companies, local governments, agriculture and others embrace drone technology

2 – Medical Prodcedures & Operations: IBM’s Watson may become the best diagnostician in the world and be greatly in demand contributing billions to IBM’s sales whilst potentially saving millions of lives. The global medical robotic systems market was worth $5.48 billion in 2011 and is expected to reach $13.6 billion in 2018, growing at a compounded annual growth rate of 12.6% from 2012. Surgical robots are expected to enjoy the largest revenue share.

3 – Robotic Prosthetics & Exoskeletons: The economic market is currently quite small, somewhere around $100 to $150 million, however with the recent advances of prosthetics and exoskeletons it is expected to grow considerably to over $1.5 billion in the next 3 to 5 years and higher still thereafter

4 – Artificial Assistants: This domain has the largest possible early impact on the largest number of people. Artificial Intelligence pioneers such as Google Director of Engineering Ray Kurzweill have indicated anyone with a smartphone or tablet will be using ‘cognitive assistants’ by 2017

5 – Driverless Cars: Autonomous vehicles, including the iconic Google self-driving cars, will be on the road commercially before 2018. The long-term impact on society of self-driving cars and other autonomous vehicles will be a radical change in how we commute. There will also likely be a sharp reduction in traffic accidents, the majority of which are caused by human error

Colin Lewis

 

Monday 28th July 2014

IBM

Watson, IBM’s (IBM:US) artificial intelligence computing platform, is changing the way we compute. From its roots as a robotic contestant on Jeopardy, the machine-learning marvel is now being positioned as a tool for doctors, businesspeople, and scientists worldwide–one that can answer any question posed to it in natural English.

IBM’s future arguably depends on Watson’s success. The company’s hardware sales have been falling for the past few years, and this computer system has been their most notable growth area. In January, IBM invested $1 billion in a massive expansion of Watson’s ecosystem.

Earlier this month, IBM announced a $3 billion R&D investment in computer hardware that mimics the human brain – Neal Ungerleider

 

Thursday 14th August 2014

Watson

Researchers at IBM are experimenting with a room where executives go to talk over business problems with a version of Watson, the computer system that defeated two Jeopardy! champions on TV in 2012.

The lab looks more or less like a normal meeting space, but with a giant display taking up one wall, and an array of microphones installed in the ceiling.

In a live demonstration, it helped researchers role-playing as executives to generate a short list of companies to acquire.

First, Watson was brought up to speed by being directed, verbally, to read over an internal memo summarizing the company’s strategy for artificial intelligence. It was then asked by one of the researchers to use that knowledge to generate a long list of candidate companies. “Watson, show me companies between $15 million and $60 million in revenue relevant to that strategy,” he said.

After the humans in the room talked over the results Watson displayed on screen, they called out a shorter list for Watson to put in a table with columns for key characteristics. After mulling some more, one of them said: “Watson, make a suggestion.” The system ran a set of decision-making algorithms and bluntly delivered its verdict: “I recommend eliminating Kawasaki Robotics.” When Watson was asked to explain, it simply added. “It is inferior to Cognilytics in every way.”

By surfacing that kind of information, Watson could change the dynamics of group interactions for the better. Watson could enhance collective intelligence by facilitating turn taking, or having a neutral presence that can help prevent groupthink. For example, people may feel freer to question their boss’s opinion if Watson is the first to suggest there is another way of looking at a problem – Tom Simonite

 

Saturday 13th December 2014

IBM’s Watson will Give You Health Advice Based on Your DNA

Maybe you have a fitness tracker. Maybe you’ve gotten your genome sequenced before. Probably your medical records are kept in electronic, instead of paper, form. Now some companies are seeking to combine all those things and more into a talking, personalized, health-advice app. Not sure when to give yourself your next insulin shot after having a croissant for breakfast? You can ask the app. How much exercise should someone with your genetic makeup be getting? The app will give you suggestions.

At least, that’s the goal of the app-makers, who include developers from IBM and a startup called Pathway Genomics. If the app, called Pathway Panorama, works as expected, it will be one of the most detailed and personalized health-advice apps we’ve ever heard of. It will bring an unprecedented amount of information to bear on the advice it gives you.

Pathway Genomics can sequence your DNA and provide an analysis as to what what those jumbled letters mean. Meanwhile, IBM’s artificial intelligence engine, Watson, will make it possible for the app to understand what users are asking it. Watson also is able to read and understand information online, so it will be able to do things like “read” published medical literature to help answer users’ questions. After all, that’s how Watson won Jeopardy, when IBM first introduced it.

Pathway expects to have the Panorama app ready by mid-2015 – Francie Diep

 

Monday 11th May 2015

Watson Artificial Intelligence

Many companies big and small are now pursuing the holy grail of artificial intelligence – at its starkest, thinking machines. Most are shrouding their efforts in secrecy, IBM isn’t.

Watson is now being marketed as a tool for people to explore and use. In New York, there’s an impressive building near the city’s so-called Silicon Alley devoted to demonstrating Watson, and finding uses for its apparent intelligence.

A new cluster of AI specialists is emerging in New York. Some of them are financial market algorithmic whizz kids redeployed after the crisis. Some are refugees from AT&T’s famous Bell Labs over the river in New Jersey. It was there that the transistor was developed in 1947. Bell Labs also did a lot of work on speech recognition for telephone networks… something that is obviously allied to machine intelligence.

At Memorial Sloan Kettering Hospital in New York a famous cancer specialist is using Watson’s data-gathering skills to expand hugely his own knowledge base, and bring him instant news of developments in his field that may be relevant to the symptoms he feeds in to it – Peter Day

 

Monday 18th May 2015

Disruption of Healthcare

By 2025, existing healthcare institutions will be crushed as new business models with better and more efficient care emerge.

Thousands of startups, as well as today’s data giants (Google, Apple, Microsoft, SAP, IBM, etc.) will all enter this lucrative $3.8 trillion healthcare industry with new business models that dematerialize, demonetize and democratize today’s bureaucratic and inefficient system.

Biometric sensing (wearables) and AI will make each of us the CEOs of our own health. Large-scale genomic sequencing and machine learning will allow us to understand the root cause of cancer, heart disease and neurodegenerative disease and what to do about it. Robotic surgeons can carry out an autonomous surgical procedure perfectly (every time) for pennies on the dollar. Each of us will be able to regrow a heart, liver, lung or kidney when we need it, instead of waiting for the donor to die – Peter Diamandis

 

Saturday 26th July 2015

THE SINGULARITY

IBM is Training Watson to be a Cancer Specialist

The idea is to use Watson’s increasingly sophisticated artificial intelligence to find personalized treatments for every cancer patient, by comparing disease and treatment histories, genetic data, scans and symptoms against the vast universe of medical knowledge.

Such precision targeting is possible to a limited extent, but it can take weeks of dedicated sleuthing by a team of researchers. Watson would be able to make this type of treatment recommendation in mere minutes.

The IBM program is one of several new aggressive health-care projects that aim to sift through the huge pools of data created by people’s records and daily routines and then identify patterns and connections to predict needs.

Instead of having to find specialists in a different city, photocopy and send all the patient’s files to them, and spend countless hours researching the medical literature, a doctor could simply consult Watson, she said.

Rob Merkel, who leads IBM Watson’s health group, said the company estimates that a single person will generate 1 million gigabytes of health-related data across a lifetime. That’s as much data as in 300 million books.

“You are deep into a realm where no human being could ever make sense of this information,” Merkel said. That’s where Watson comes in to create a “collective intelligence model between machine and man.”

“We’re not advocating that Watson replace physicians,” he explained. “We are advocating that Watson does a lot of reading on behalf of physicians and provides them with timely insights.”

In 2011, IBM announced that Watson had learned as much as a second-year medical student. Since then it’s graduated and has been doing residencies at some of the nation’s top cancer centers. In late September, Watson achieved another training milestone: It began its first fellowship in a specialty — leukemia — at MD Anderson.

Koichi Takahashi, at the top of last year’s class of fellows and recently appointed an assistant professor, said he’s been impressed so far. Watson’s ability to synthesize a patient’s history is “amazing,” Takahashi said. “He beats me.” – Ariana Eunjung Cha

 

Tuesday 29th December 2015

Building The Quantum Dream Machine

John Martinis has been researching how quantum computers could work for 30 years. Now he could be on the verge of finally making a useful one.

With his new Google lab up and running, Martinis guesses that he can demonstrate a small but useful quantum computer in two or three years. “We often say to each other that we’re in the process of giving birth to the quantum computer industry,” he says.

The new computer would let a Google coder run calculations in a coffee break that would take a supercomputer of today millions of years.

The software that Google has developed on ordinary computers to drive cars or answer questions could become vastly more intelligent. And earlier-stage ideas bubbling up at Google and its parent company, such as robots that can serve as emergency responders or software that can converse at a human level, might become real.

As recently as last week the prospect of a quantum computer doing anything useful within a few years seemed remote. Researchers in government, academic, and corporate labs were far from combining enough qubits to make even a simple proof-of-principle machine.

A well-funded Canadian startup called D-Wave Systems sold a few of what it called “the world’s first commercial quantum computers” but spent years failing to convince experts that the machines actually were doing what a quantum computer should.

Then NASA summoned journalists to building N-258 at its Ames Research Center in Mountain View, California, which since 2013 has hosted a D-Wave computer bought by Google.

There Hartmut Neven, who leads the Quantum Artificial Intelligence lab Google established to experiment with the D-Wave machine, unveiled the first real evidence that it can offer the power proponents of quantum computing have promised.

In a carefully designed test, the superconducting chip inside D-Wave’s computer—known as a quantum annealer—had performed 100 million times faster than a conventional processor.

However, this kind of advantage needs to be available in practical computing tasks, not just contrived tests. “We need to make it easier to take a problem that comes up at an engineer’s desk and put it into the computer,” said Neven.

That’s where Martinis comes in. Neven doesn’t think D-Wave can get a version of its quantum annealer ready to serve Google’s engineers quickly enough, so he hired Martinis to do it.

“It became clear that we can’t just wait,” Neven says. “There’s a list of shortcomings that need to be overcome in order to arrive at a real technology.”

He says the qubits on D-Wave’s chip are too unreliable and aren’t wired together thickly enough. (D-Wave’s CEO, Vern Brownell, responds that he’s not worried about competition from Google.)

Google will be competing not only with whatever improvements D-Wave can make, but also with Microsoft and IBM, which have substantial quantum computing projects of their own.

But those companies are focused on designs much further from becoming practically useful. Indeed, a rough internal time line for Google’s project estimates that Martinis’s group can make a quantum annealer with 100 qubits as soon as 2017.

The difficulty of creating qubits that are stable enough is the reason we don’t have quantum computers yet. But Martinis has been working on that for more than 11 years and thinks he’s nearly there.

The coherence time of his qubits, or the length of time they can maintain a superposition, is tens of microseconds—about 10,000 times the figure for those on D-Wave’s chip.

Martinis aims to show off a complete universal quantum computer with about 100 qubits around the same time he delivers Google’s new quantum annealer, in about two years.

He thinks that once he can get his qubits reliable enough to put 100 of them on a universal quantum chip, the path to combining many more will open up. “This is something we understand pretty well,” he says. “It’s hard to get coherence but easy to scale up.”

Figuring out how Martinis’s chips can make Google’s software less stupid falls to Neven.

He thinks that the prodigious power of qubits will narrow the gap between machine learning and biological learning—and remake the field of artificial intelligence. “Machine learning will be transformed into quantum learning,” he says. That could mean software that can learn from messier data, or from less data, or even without explicit instruction.

Neven muses that this kind of computational muscle could be the key to giving computers capabilities today limited to humans. “People talk about whether we can make creative machines–the most creative systems we can build will be quantum AI systems,” he says.

Neven pictures rows of superconducting chips lined up in data centers for Google engineers to access over the Internet relatively soon.

“I would predict that in 10 years there’s nothing but quantum machine learning–you don’t do the conventional way anymore,” he says.

A smiling Martinis warily accepts that vision. “I like that, but it’s hard,” he says. “He can say that, but I have to build it.” – Tom Simonite

 

Wednesday 20th January 2016

Softbank’s Pepper Robot to Get Smarter with IBM’s Watson AI

When it launched last year, SoftBank’s emotion-reading robot Pepper sold out in just one minute despite its limited utility.

Now, Pepper’s about to get smarter thanks to a partnership with IBM to integrate Watson cognitive system into its brains.

With Watson, developers hope to help Pepper understand human emotions more thoroughly to appropriately respond and engage with its users.

Pepper is intended to be used in an enterprise setting so businesses can employ Pepper to greet customers or help at self-service kiosks. The two companies are also exploring ways to use Pepper in medical assistance and education. – Natt Garlin

 

Sunday 13th March 2016

Investing in Robotics and AI Companies

Here are some AI (and robotics) related companies to think about.

I’m not saying you should buy them (now) or sell for that matter, but they are definitely worth considering at the right valuations.

Think about becoming an owner of AI and robotics companies while there is still time. I plan to buy some of the most obvious ones (including Google) in the ongoing market downturn (2016-2017).

Top 5 most obvious AI companies

  • Alphabet (Google)
  • Facebook (M, Deep Learning)
  • IBM (Watson, neuromorphic chips)
  • Apple (Siri)
  • MSFT (skype RT lang, emo)
  • Amazon (customer prediction; link to old article)

Yes, I’m US centric. So sue me 🙂

Other

  • SAP (BI)
  • Oracle (BI)
  • Sony
  • Samsung
  • Twitter
  • Baidu
  • Alibaba
  • NEC
  • Nidec
  • Nuance (HHMM, speech)
  • Marketo
  • Opower
  • Nippon Ceramic
  • Pacific Industrial

Private companies (*I think):

  • *Mobvoi
  • *Scaled Inference
  • *Kensho
  • *Expect Labs
  • *Vicarious
  • *Nara Logics
  • *Context Relevant
  • *MetaMind
  • *Rethink Robotics
  • *Sentient Technologies
  • *MobileEye

General AI areas to consider when searching for AI companies

  • Self-driving cars
  • Language processing
  • Search agents
  • Image processing
  • Robotics
  • Machine learning
  • Experts
  • Oil and mineral exploration
  • Pharmaceutical research
  • Materials research
  • Computer chips (neuromorphic, memristors)
  • Energy, power utilities

Mikael Syding

 

Sunday 13th March 2016

AI is Coming

Artificial Intelligence keeps progressing, no matter whether you know about (or like it) or not.

First an AI application is typically seen as a curiosity. Then it becomes a tool you need to learn how to use. And eventually it will develop to the point where it could take your job.

IBM’s Watson easily beat the world’s best Jeopardy masters several years ago. Since then it has become the world’s foremost oncology expert.

Currently Watson is on its way to start replacing swathes of paralegals at law firms as well as finding new oil reserves.

A few years down the road, anyone with a cellphone (or AugReal contact lens) will be able to tap into Watson-like powers for any kind of search or research. – Mikael Syding

Sunday 13th March 2016

Anticipated Top AI Breakthroughs: 2016 – 2018

At A360 this year, my expert on AI was Stephen Gold, the CMO and VP of Business Development and Partner Programs at IBM Watson.

AI Progress of late is furious — an R&D arms race is underway among the world’s top technology giants.

Soon AI will become the most important human collaboration tool ever created, amplifying our abilities and providing a simple user interface to all exponential technologies. Ultimately, it’s helping us speed toward a world of abundance.

The implications of true AI are staggering,

“It’s amazing,” said Gold. “For 50 years, we’ve ideated about this idea of artificial intelligence. But it’s only been in the last few years that we’ve seen a fundamental transformation in this technology.”

Here are Gold’s predictions for the most exciting, disruptive developments coming in AI in the next three years. As entrepreneurs and investors, these are the areas you should be focusing on, as the business opportunities are tremendous.

1 – Next-gen A.I. systems will beat the Turing Test

Alan Turing created the Turing Test over half a century ago as a way to determine a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human.

Loosely, if an artificial system passed the Turing Test, it could be considered “AI.”

Gold believes, “that for all practical purposes, these systems will pass the Turing Test” in the next three-year period.

Perhaps more importantly, if it does, this event will accelerate the conversation about the proper use of these technologies and their applications.

2 – Leverage ALL health data (genomic, phenotypic, social) to redefine the practice of medicine.

“I think AI’s effect on healthcare will be far more pervasive and far quicker than anyone anticipates,” says Gold. “Even today, AI/machine learning is being used in oncology to identify optimal treatment patterns.”

But it goes far beyond this. AI is being used to match clinical trials with patients, drive robotic surgeons, read radiological findings and analyze genomic sequences.

3 – AI will be woven into the very fabric of our lives — physically and virtually.

Ultimately, during the AI revolution taking place in the next three years, AIs will be integrated into everything around us, combining sensors and networks and making all systems “smart.”

AIs will push forward the ideas of transparency, of seamless interaction with devices and information, making everything personalized and easy to use. We’ll be able to harness that sensor data and put it into an actionable form, at the moment when we need to make a decision.

Peter Diamandis

 

Tuesday 29th March 2016

$3bill of Artificial Intelligence R&D Planned in South Korea 

After what has been dubbed the ‘AlphaGo shock’, South Korea is getting serious about artificial intelligence

South Korea, well known for its IT infrastructure, is promising 3.5 trillion won ($3 billion) in funding from the public and private sectors to develop artificial intelligence for corporate and university AI projects.

South Korea’s President Park Geun-hye assembled leaders across the country’s tech industry and senior government officials in Seoul last week to announce plans to invest the amount over the next five years.

It appears to be largely a reaction to the phenomenal performance of Google’s algorithm AlphaGo in an historic AI-versus-human game in Seoul earlier this month, which captured the South Korean media’s imagination.

“Above all, Korean society is ironically lucky, that thanks to the ‘AlphaGo shock’ we have learned the importance of AI before it is too late,” the president told local reporters assembled for the meeting, describing the game as a watershed moment of an imminent “fourth industrial revolution”.

South Korea will establish a new high-profile, public/private research centre with participation from several Korean conglomerates, including Samsung, LG, telecom giant KT, SK Telecom, Hyundai Motor, and internet portal Naver.

The institute was reportedly already in the works, but AlphaGo’s domination quickened the process of setting up the grouping. Some Korean media reports indicate that the institute could open its doors as early as 2017.

South Korea already funds two high-profile AI projects — Exobrain, which is intended to compete with IBM’s Watson computer, and Deep View, a computer vision project. – Philip Iglauer

 

Sunday 24th April 2016

AI Hits the Mainstream

Insurance, finance, manufacturing, oil and gas, auto manufacturing, health care: these may not be the industries that first spring to mind when you think of artificial intelligence. But as technology companies like Google and Baidu build labs and pioneer advances in the field, a broader group of industries are beginning to investigate how AI can work for them, too.

Today the industry selling AI software and services remains a small one. Dave Schubmehl, research director at IDC, calculates that sales for all companies selling cognitive software platforms —excluding companies like Google and Facebook, which do research for their own use—added up to $1 billion last year.

He predicts that by 2020 that number will exceed $10 billion. Other than a few large players like IBM and Palantir Technologies, AI remains a market of startups: 2,600 companies, by Bloomberg’s count.

General Electric is using AI to improve service on its highly engineered jet engines. By combining a form of AI called computer vision (originally developed to categorize movies and TV footage when GE owned NBC Universal) with CAD drawings and data from cameras and infrared detectors, GE has improved its detection of cracks and other problems in airplane engine blades.

The system eliminates errors common to traditional human reviews, such as a dip in detections on Fridays and Mondays, but also relies on human experts to confirm its alerts. The program then learns from that feedback, says Colin Parris, GE’s vice president of software research. – Nanette Byrnes

 

Sunday 24th April 2016

A $2 Billion Chip to Accelerate Artificial Intelligence

Two years ago we were talking to 100 companies interested in using deep learning. This year we’re supporting 3,500. In two years there has been 35X growth. – Jen-Hsun Huang, CEO of Nvidia

The field of artificial intelligence has experienced a striking spurt of progress in recent years, with software becoming much better at understanding images, speech, and new tasks such as how to play games. Now the company whose hardware has underpinned much of that progress has created a chip to keep it going.

Nvidia announced a new chip called the Tesla P100 that’s designed to put more power behind a technique called deep learning. This technique has produced recent major advances such as the Google software AlphaGo that defeated the world’s top Go player last month.

Deep learning involves passing data through large collections of crudely simulated neurons. The P100 could help deliver more breakthroughs by making it possible for computer scientists to feed more data to their artificial neural networks or to create larger collections of virtual neurons.

Artificial neural networks have been around for decades, but deep learning only became relevant in the last five years, after researchers figured out that chips originally designed to handle video-game graphics made the technique much more powerful. Graphics processors remain crucial for deep learning, but Nvidia CEO Jen-Hsun Huang says that it is now time to make chips customized for this use case.

At a company event in San Jose, he said, “For the first time we designed a [graphics-processing] architecture dedicated to accelerating AI and to accelerating deep learning.” Nvidia spent more than $2 billion on R&D to produce the new chip, said Huang.

It has a total of 15 billion transistors, roughly three times as many as Nvidia’s previous chips. Huang said an artificial neural network powered by the new chip could learn from incoming data 12 times as fast as was possible using Nvidia’s previous best chip.

Deep-learning researchers from Facebook, Microsoft, and other companies that Nvidia granted early access to the new chip said they expect it to accelerate their progress by allowing them to work with larger collections of neurons.

“I think we’re going to be able to go quite a bit larger than we have been able to in the past, like 30 times bigger,” said Bryan Catanzero, who works on deep learning at the Chinese search company Baidu. Increasing the size of neural networks has previously enabled major jumps in the smartness of software. For example, last year Microsoft managed to make software that beats humans at recognizing objects in photos by creating a much larger neural network.

Huang of Nvidia said that the new chip is already in production and that he expects cloud-computing companies to start using it this year. IBM, Dell, and HP are expected to sell it inside servers starting next year. – Tom Simonite

 

Saturday 18th June 2016

Tech Moguls Declare Era of Artificial Intelligence

Amazon CEO Jeff Bezos predicted a profound impact on society over the next 20 years.

“It’s really early but I think we’re on the edge of a golden era. It’s going to be so exciting to see what happens,” he said.

Amazon has been working on artificial intelligence for at least four years and now has 1,000 employees working on Alexa, the company’s voice-based smart assistant software system, he said.

Big tech companies including Amazon have an edge at present because they have access to large amounts of data but hundreds of AI startups will hatch in the next few years, he said.

IBM CEO Ginni Rometty said the company has been working on artificial technology, which she calls a cognitive system, since 2005 when it started developing its Watson supercomputer.

“I would say in five years, there’s no doubt in my mind that cognitive AI will impact every decision made” from healthcare to education to financial services, Rometty said. – Liana B. Baker

 

Saturday 18th June 2016

The Singularity is Near

When Ray Kurzweil published The Singularity Is Near in 2006, many scoffed at his outlandish predictions.

A year before Apple launched its iPhone, Kurzweil imagined a world in which humans and computers essentially fuse, unlocking capabilities we normally see in science fiction movies.

He pointed out that as technology accelerates at an exponential rate, progress would eventually become virtually instantaneous—a singularity. Further, he predicted that as computers advanced, they would merge with other technologies, namely genomics, nanotechnology and robotics.

Today, Kurzweil’s ideas don’t seem quite so outlandish. Google’s DeepMind recently beat legendary Go world champion Lee Sedol. IBM’s Watson is expanding horizons in medicine, financial planning and even cooking. Self driving cars are expected to be on the road by 2020.

Just as Kurzweil predicted, technology seems to be accelerating faster than ever before. – Greg Satell

 

Thursday 18th August 2016

The AI Gold Rush

Companies are lining up to supply shovels to participants in the AI gold rush.

The name that comes up most frequently is NVIDIA (NASDAQ: NVDA), says Chris Dixon of Andreessen Horowitz; every AI startup seems to be using its GPU chips to train neural networks.

GPU capacity can also be rented in the cloud from Amazon (NASDAQ: AMZN) and Microsoft (NASDAQ: MSFT).

IBM (NYSE: IBM) and Google, meanwhile, are devising new chips specifically built to run AI software more quickly and efficiently.

And Google, Microsoft and IBM are making AI services such as speech recognition, sentence parsing and image analysis freely available online, allowing startups to combine such building blocks to form new AI products and services.

More than 300 companies from a range of industries have already built AI-powered apps using IBM’s Watson platform, says Guru Banavar of IBM, doing everything from filtering job candidates to picking wines. – The Economist

 

Thursday 18th August 2016

Watson AI Reduces IBM’s Digital Ad Costs by 35%

  • Watson reduced IBM’s cost per click on average by 35%.
  • At its best, using Watson reduced the cost per click for IBM by 71% when compared to its previous buying methods.
  • About $27 billion will be spent on programmatic display ads in 2017, a 24% increase from the previous year

For almost a year now the company has been trying out Watson, its cognitive computer that learns and makes snap decisions on the fly, on making programmatic ad buys for a portion of its online ad campaigns.

The results proved so effective that IBM now says it will use Watson for all of its programmatic campaigns by the end of the year, a big commitment for a company that spent nearly $53 million on digital display advertising in 2014.

“Because of the volume and the dollars involved, trying to save those fractions of a dollar, or fractions of a cent, really matters to us,” said Ari Sheinkin, VP of marketing analytics at IBM. “What makes this really exciting is the system learns. That’s the essence of cognitive.”

IBM is working on making the technology available for agencies and exchanges so they, too, can eventually maximize ROI through what IBM calls “cognitive bid optimization.”

“It’s almost as if the system we’ve created right now is like a child prodigy who is already beating you at chess,” Mr. Sheinkin said. “But it’s actually only starting to learn. It’s only had so many campaigns to learn from, so it’s going to get better and better because every time it runs it learns from the last thing it did. And that is the most important, distinguishing feature.” – George Slefo

 

Friday 30th September 2016

AI Saves Life By Identifying Disease When Humans Failed

  • Japanese doctors have, for the first time in history, used artificial intelligence to detect a type of leukemia.

Image result for ai healthcare

If you needed proof that the age of artificial intelligence is officially upon us, well, look no farther.

Reports assert that IBM’s artificial intelligence (AI) system, Watson, just saved the life of a Japanese woman by correctly identifying her disease. This is notable because, for some time, her illness went undetected using conventional methods, and doctors were stumped.

The AI’s positive identification allowed doctors to develop a treatment for the woman in question, ultimately saving her life.

The key to this success is the AI’s ability to take a massive amount of data and analyze it quickly. This is something that human physicians, sadly, cannot do themselves (or at least, they can’t do it with nearly the accuracy or efficiency).

The system looked at the woman’s genetic information and compared it to 20 million clinical oncology studies. After doing so, it determined that the patient had an exceedingly rare form of leukemia.

Initially, the woman had been diagnosed with, and treated for, acute myeloid leukemia; however, she failed to respond to the traditional treatment methods, which perplexed doctors.

Notably, the AI was able to diagnose the condition in just 10 minutes. – Jolene Creighton

 

Wednesday 30th November 2016

Building an AI Portfolio

The following stocks offer exposure to Artificial Intelligence. – Lee Banfield

——————————-

Google (NASDAQ: GOOGL)

Stock Price: $776

Market Cap: $531 billion

Image result for google deepmind

Healthcare Images – Google Deepmind

Machine Learning – GoogleML

Autonomous Systems – Google Self-driving Car

Hardware – GoogleTPU

Open Source Library – TensorFlow

IBM (NYSE: IBM)

Stock Price: $162

Market Cap: $154 billion

Image result for ibm watson

Enterprise Intelligence – IBM Watson

Healthcare – IBM Watson Health

Amazon (NASDAQ: AMZN)

Stock Price: $752

Market Cap: $355 billion

Image result for amazon alexa logo

Personal Assistant – Amazon Alexa

Open Source Library – DSSTNE

Microsoft (NASDAQ: MSFT)

Stock Price: $60

Market Cap: $473 billion

Image result for cortana logo

Personal Assistant – Cortana

Open Source Libraries – CNTK, AzureML, DMTK

Nvidia (NASDAQ: NVDA)

Stock Price: $94

Market Cap: $50 billion

Image result for nvidia

Hardware

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Wednesday 14th December 2016

IBM: In 5 years, Watson A.I. Will be Behind Your Every Decision

  • IBM has invested billions of dollars in its Watson business unit, created at the start of 2014, which now employs an estimated 10,000 workers. – Steve Lohr

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In the next five years, every important decision, whether it’s business or personal, will be made with the assistance of IBM Watson. That’s the vision of IBM president and CEO Ginni Rometty

“Our goal is augmenting intelligence,” Rometty said. “It is man and machine. This is all about extending your expertise. A teacher. A doctor. A lawyer. It doesn’t matter what you do. We will extend it.”

Rometty also said IBM’s moonshot is focused on healthcare, and she brought on stage Satoru Miyano, a professor of the Human Genome Center at the Institute of Medical Science in the University of Tokyo.

Miyano said researchers and doctors are faced with too much data. Last year, he said, more than 200,000 papers were published about cancer alone. Meanwhile, 4 million cancer mutations also were reported.

“Nobody can read it all, but Watson can read, understand and learn. Why not use it?” Miyano said.

Miyano gave the example of a 66-year-old woman with leukemia. She was receiving standard therapy but was still getting worse. Doctors didn’t understand why she was getting sicker.

Using Watson, researchers analyzed all of the data they had on the woman in 10 minutes.

“Watson’s results were investigated, targeting specific genes,” Miyano said. “The team found she had another type of leukemia [that needed] a different therapy. She got it and she recovered completely.” – Sharon Gaudin

 

Wednesday 14th December 2016

“In 30% of the Cancer Cases, Watson Found a Treatment the Human Doctors Missed”

IBM first focused on health care, and that business now accounts for two-thirds of the Watson unit’s employment.

Three years ago, IBM experts began working with leading medical centers. And it has spent more than $4 billion buying a handful of companies with vast stores of medical data like billing records, patient histories, and X-ray and M.R.I. images.

At the University of North Carolina School of Medicine, Watson was tested on 1,000 cancer diagnoses made by human experts. In 99 percent of them, Watson recommended the same treatment as the oncologists.

In 30 percent of the cases, Watson also found a treatment option the human doctors missed. Some treatments were based on research papers that the doctors had not read — more than 160,000 cancer research papers are published a year. Other treatment options might have surfaced in a new clinical trial the oncologists had not yet seen announced on the web.

But Watson read it all. “Humans enabled by A.I. is the way to go with genomics,” said Dr. Norman E. Sharpless, head of the school’s cancer center. – Steve Lohr

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