Category Archives: Artificial Intelligence

There are many different use cases for artificial intelligence, even though most of them have yet to be explored. A Vanderbilt University data scientist has come up with a bold and radical plan to deploy AI as a way to predict suicide. That is a rather remarkable turn of events, as it could yield quite positive results. Giving others a chance to prevent people from committing suicide is invaluable, that much is evident.

On paper, it makes a lot of sense to use artificial intelligence as well as any other form of technology to prevent suicide attempts from happening. Colin Walsh, data scientists at Vanderbilt University Medical Center, is thinking along the same lines. To be more specific, he feels AI can play a key role in the future of predicting suicide risk and giving loved ones a chance to stop people from ending their life prematurely.

As of right now, Walsh and other scientists have successfully developed a machine-learning algorithm to predict the likelihood of people attempting suicide. As one would expect from such innovative technology, the algorithm is more than capable of accurately predicting these attempts. In fact, some people claim this algorithm is unnervingly accurate, which is both good and bad.

To be put this into numbers people can understand, the algorithm is between 80% and 90% accurate. It is not a bad thing to get some false positives, though, as long as it means the patient will not attempt suicide whatsoever. Failing to predict when someone would effectively attempt suicide is a factor to be a quite concerned about, though the much is evident. These results pertain to the patients likelihood to commit suicide in the next two years.

When reducing the timespan associated with this investigation, the results become a lot more accurate. More specifically, when assessing if a patient is likely to attempt suicide within the next week, the algorithm has a 92% accuracy rate. Do keep in mind all of these results are based on data widely available from hospital admissions, including patients age, gender, medications, and prior diagnoses.

So far, the team has gathered enough data from 5,617 patients to develop this algorithm. A total of 3,250 instances of suicide attempts has been recorded as a result. All of the patients in question were admitted with signs of self-harm, which is a primary indicator of future suicide attempts. Although this is still a relatively small sample size, it also goes to show the algorithm developed by the team of data scientists is definitely worth keeping an eye on.

It is evident artificial intelligence can be a valuable tool when it comes to preventing people from attempting suicide. Although this experiment is still in the early stages of development, it will be interesting to see if and whether researchers can improve upon it moving forward. Interestingly enough, a different algorithm was created to conduct similar tests looking at over 12,000 randomly selected patients with no documented history of self-harm. In this case, the algorithm was even more accurate, which is rather surprising.

Rest assured some people will feel the usage of artificial intelligence is an invasion of privacy, even if it can reduce the number of suicide attempts. There is a lot of data gathered by hospitals, which can be used for this purpose, without having to collect additional information from patients. It will be interesting to see how these algorithms evolve over time, and whether or not artificial intelligence will effectively be used to prevent suicide attempts in the future.

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Data Scientists use Artificial Intelligence to Predict Suicide Attempts - The Merkle

Forbes

Half Of People Who Encounter Artificial Intelligence Don't Even Realize It Forbes Artificial Intelligence (AI) is no longer in the future. It's not science fiction. It's here. It's now. It's happening all around us, and actually has been for more years than most of us even know. For the past two years, I've been writing about IBM's ...

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Half Of People Who Encounter Artificial Intelligence Don't Even Realize It - Forbes

Age: 54

From: Mumbai

In cloud we trust: CEO since 2007, Shantanu Narayen has overseen a period of explosive growth for the San Jose software company. As Adobe ( adbe ) has embraced a cloud-based subscription model, its stock has been on a tear, up 43% (to $142) since late May, with annual revenues of $5.85 billion.

Foggy bottom: When Narayen became CEO, you could see there were some dark clouds on the horizon, he says. The global financial crisis was just around the corner, and Adobe was not landing new customers as fast as desired. I didnt time that very well, Narayen jokes.

Outside the box: By 2009, Adobe embarked on an ambitious mission to overhaul the way it shipped popular products like Photoshop. A crisis is a terrible thing to waste, Narayen says. Adobe switched to a subscription model, opening the door to a new way to deliver software in which customers could more easily receive updates and new features.

Finding Wall Street: Investors were concerned Adobe was spending too much on data centers, but Narayen convinced them it would pay off. I think we did a good job of that, Narayen says. By going to the cloud , Adobe ended up saving money with the switch from one-time licenses to recurring subscriptions. Narayen adds that ditching packaging also helped.

The next frontier: Narayen sees artificial intelligence as a game changer, but he warns, Many companies just say A.I. without understanding how they want to apply it. Adobes A.I. plans start with voice commands. Imagine brightening colors on photos just by speaking.

Double Duty: Adobes board elected Narayen as its chairman this year on top of his CEO duties. Narayen is quick to mention Adobe couldnt be successful without his staffs hard work. But, he says, maybe it is recognition of some of the contributions Ive made in the company.

A version of this article appears in the June 15, 2017 issue of Fortune with the headline "Flash Forward."

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Adobe CEO Hints at Artificial Intelligence on Photoshop - Fortune

The theatrical release of James Camerons sci-fi film Terminator 2, featuring Arnold Schwarzenegger as a cyborg with a computer brain, had a crucial scene deleted. The scene, part of the extended release of the movie, shows young John Connor and his mother opening up the head of the cyborg to switch its computer brain from read only to learning mode. The cyborg (Schwarzenegger) then picks up human values and mannerisms as the movie progresses.

For movie buffs, the deleted scene is worth seeing for special effects and also to catch a glimpse of Linda Hamilton (playing Johns mother Sarah Connor) with her twin sister Leslie playing her image in a mirror. In the theatrical release, where the scene is omitted, the cyborg just tells John that its brain is a neural-net processor, a learning computer, without mentioning any on/off options. That was back in 1991. Today, in 2017, a learning computer is much more of a reality.

While artificial intelligence (AI) and machine learning (ML) concepts have been around since the 1940s and 1950s (See ABC of AI, ML and Deep Learning), the availability of huge amounts of data is making the difference now. A learning computer does not need to travel back in time like in the movie and many are solving real problems in India. For example, in healthcare, ML is helping oncologists sift through huge amounts of cancer cases and suggesting preferred treatment; in education it is predicting who might drop out of school; and in fashion it is forecasting colours that can dominate the next season. Retail, transportation and financial services have adopted ML in different forms. The learning switch is turned on in India. Every large organisation was sitting on data. The cloud is bringing computing power to it and ML is creating actionable intelligence, says Anil Bhansali, MD, Microsoft India (R&D) Pvt Ltd.

Machine vs machine A war of machines scenario seems appropriate to discuss it. Consider this example. In October 2016, K Sandeep Nayak booked three flight tickets for his wife and children to fly to Mangaluru from Mumbai during the Christmas holidays two months later, hoping to get a low fare. He spent Rs 7,500 per ticket. Later, when he decided to join his family for the trip, just a day before the journey on December 25, he could book himself into the same flight at Rs 4,000 only. I wish I could find out if airfare could fall, says Nayak, an executive director with Centrum Broking.

Actually, there is a way.

Today, most airlines follow a sinusoidal graph (S curve) for pricing tickets, often dictated by an algorithm to maximise revenues pushing up prices following buying behaviour.gregator app Ixigo. It can predict whether the price of an air ticket on a particular date is likely to fall. When a customer enters the date of journey, the app predicts, with more than 80 per cent accuracy, how much the airfare may drop for the sector on that date and how the prices could vary over that period. (Ixigo also has a railway app that predicts if a rail ticket on a wait list may get confirmed.)

We have a huge data set created by 4 million active users, 50 million sessions per month, Aloke Bajpai, Ixigo

Ixigos global peer Kayak is one of the pioneers in fare prediction. If airfare prediction seems like a machine-vs-machine scenario, there are more such examples: programmatic advertising algorithms that compete for advertising spots, or algorithmic trading applications that compete to get the best trades in the securities market.

Here is something a little more interesting.

Arya.ai is a Mumbaibased startup, founded by Vinay Kumar and Deekshith Marla, both IIT-Bombay grads. In 2016, Arya.ai was selected by French innovation agency Paris&Co, from 21 global companies, for an international innovation award. Kumar still looks like a college student and moves around Mumbai on his motorbike. One of the current projects that Arya.ai is working on involves creating an ML application for selling securities without letting prices crash. The client, with a mandate to sell a large block of stock or bonds in the market, wants Arya.ai to create an algorithm for selling so that it does not lead to prices of the security dropping.

At the same time, there are ML algorithms as well as human intelligence trying to buy the security at the lowest price possible, says Kumar. Algorithmic trading has been around for a while and brokers with proprietary trading arms often use it to gain a few seconds advantage. Now research is focused on whether an ML layer can be built on top of the algo. Can the machines be allowed to alter the trading algorithm on their own and what will this mean for the securities markets?

Last month, JP Morgan released a report in New York, Big Data and AI Strategies, with the subhead, Machine Learning and Alternative Data Approach to Investing

Written by Marko Kolanovic and Rajesh T Krishnamachari, the report suggests that analysts and market operators need to master ML techniques as usual indicators like company quarterly reports and GDP growth data will soon be predicted early by ML programs. It says that just as machines with ML are able to replace humans for short-term trading decisions, they can also do better than humans in the medium term. Machines have the ability to quickly analyse news feeds and tweets, process earnings statements, scrape websites and trade on these instantaneously. Back in India, here is another scenario. Vertoz is a Mumbai-based programmatic advertising company that works with clients (advertisers) and online media in placing digital advertising, targeting the advertisements and bidding for the best spots.

We need to find which inventory is good for us, says founder Ashish Shah, referring to spots on popular media websites. If we had to do it manually it would be like finding needles in a haystack. Vertozs programs compete with the likes of Google, bidding for top slots in global digital media.

Man Fridays While the buzz on big data analytics came first, the focus on ML has been facilitated by larger players like Google, Intel, Microsoft and Amazon making off-the-shelf modules available in India. But, then, some platforms have been around for decades. Says Shah: Most of our work is based on Java and Python that are 1980s technologies. We have built our layers on top of that.

Ixigos chief technology officer Rajnish Kumar mentions Googles TensorFlow and Amazons AWS Machine Learning as examples of off-the-shelf modules. Microsoft offers its Azure platform for others to create their own ML offerings. A Google spokesperson told ET Magazine that in future it expects to offer non-experts the ability to create and deploy ML modules: At Google, we have applied deep learning models to many applications from image recognition to speech recognition to machine translation. In our approach a controller neural net can propose a child model architecture, which can then be trained and evaluated for quality on a particular task. This is machine to machine learning.

Going forward, we will work on careful analysis and testing of these machine-generated architectures to refine our understanding. If we succeed, we think this can inspire new types of neural nets and make it possible for non-experts to create neural nets tailored to their particular needs, allowing machine learning to have a greater impact on everyone, adds the Google spokesperson. Google offers some simple applications of ML. CESC Ltd, Kolkata-based flagship of the RP-Sanjiv Goenka Group, is using a Google API (application programming interface) which records the reading of the electrical metre when the numbers are read out loud. Instead of keying the reading in or taking a photo of it, the staff can speak into their phone app chaar-shunyo-teen-paanch (4, 0, 3, 5), says Debashis Roy, vice-president (information technology ), CESC Ltd.

Roy says that when the project started, the app showed only 40% accuracy, but it is learning to recognise more and more Bengali dialects as well as Hindi and English. No matter what the dialect of the staff, the reading can be recorded. We will launch it fully when we get to 95% accuracy, says Roy.

Another Google partner is Pune-based Searce, a 12-year-old operation led by founder Hardik Parekh, who finds it convenient to work with Googles APIs as he feels the company almost embodies the open source or democratic spirit. Parekhs ML offering HappierHR tries to automate much of the routine HR operations right from initial interviews of job applicants and induction of new employees to creation of their email ids and leave approvals.

Supervisors also get suggestions to give leave to subordinates on, say, their wedding anniversaries, if there arent any important meetings scheduled for that day,says Parekh. While Google, Amazon and Microsoft offer platforms for others to use, IBM has its own ML suite called Watson, a complete offering at the premium end of the market for end-users. One of the earliest projects IBM took up in India was with Manipal Hospitals in oncology. Manipal was an early adopter: it was globally the second or the third hospital to adopt it, says Prashant Pradhan, chief developer advocate for IBM in India and South Asia.

This is how it works. For a medical board on breast cancer, the Watson program is made a member along with other doctors. Given a specific case, Watson gives its opinion and preferred treatment after going through millions of cases that are loaded on to it. Entire cancer research can run to 50 million pages, and 40,000 papers are added every year.

It is impossible for a doctor to go through all of that. The ratio of cases to oncologists is 16,000:1, adds Pradhan, stressing why ML is a great application to use in cancer treatment. Microsoft, too, has used its ML offerings in Indias healthcare. In Hyderabad, it has helped LV Prasad Eye Institute treat avoidable blindness. A second project it has worked on is helping children who wear glasses.

The work started in India has gone global, and LV Prasad Eye Institute is now part of the Microsoft Intelligent Network for Eyecare, which includes five other eyecare facilities from across the world. Microsoft has also studied 50,000 students in Class X in Chittoor in Andhra Pradesh to predict which ones may drop out. It allows the schools to send them for counselling.

Machine radiologists and bankers There is enough indication that ML bots or apps can often deliver better results than humans. Last month, IT services giant Wipro said it got productivity of 12,000 people out of 1,800 bots (software programs that perform automated tasks). Automated bots are not quite ML, but are an indicator of what may come. Rizwan Koita, serial entrepreneur and founder CEO of Citius Tech, a healthcare-focused tech company, recalls a conversation with his niece two months ago. She had qualified to pursue a course in radiology or anaesthesiology and was seeking my advice. I had to tell her that in a few years a radiologist may not have a job, says Koita. He argues that a radiologists job is to interpret images. Therefore millions of existing images (X-rays, sonograms, scans) and their interpretations can be fed into an ML algorithm; it may be a matter of time before a machine gives better interpretations than a human radiologist.

From healthcare to fashion. Mumbai-based designer couple Shane and Falguni Peacock have been using IBMs Watson for a couple of months now. The system helps the duo go through designs and silhouettes that have been shown at fashion events across the world over the last decade. They are using Watson for a project that uses international designs in Bollywood. Watson predicts colours that may be in vogue six months from now and warns if certain silhouettes have been overused in the last couple of years.

Watson is able to tell us what colour may be in six months from now, says Shane Peacock

Says Shane: Suppose we want to work on a Mughal theme, we can feed images of Mughal-era paintings, architectures and colours into the system, which is able to turn out its unique prints. It also reproduces Mughal prints created by other human designers, just for comparison. The designer couple have one more exciting project for which they are using Watson. A dress that changes hues according to the time of the day or the mood of the person wearing it. We can use two colours, say black and white. The dress can become fully white or fully black or a combination of black and white. An app on the wearers phone can control it. The change can happen on the go, while the dress is worn. You can get into a car in white and come out in black. In financial services, Kumar of Arya.ai points out that the loan approval process is an area where he sees a lot of human effort being bested by machines. In fact, Arya has implemented a program where an ML app sifts through loan applications.

ICICI Lombard and Birla Sun Life Insurance too have created bots as the first interface with customers Not to be left behind, the Indian IT biggies, TCS, Infosys and Wipro, have their own ML and AI offerings (See Machine Learning in India). Google announced in March that it will mentor half a dozen AI startups. A report by Tracxn, a venture capital research platform, noted that there are at least 300 startups in India using ML and AI technologies. An opportunity also presents a threat. Before ML can replace humans in core functions, it will need humans to create applications. Says Bhansali of Microsoft: These are still early days: technologies are on trial and talent is scarce. Ixigo CEO Aloke Bajpai echoes him when he says there are no trained engineers in AI and ML in India, and his team is entirely trained in-house.

There is definitely a shortage of talent for AI technologies. Only 4 per cent of AI professionals in India have worked on core AI technologies such as deep learning and neural networks, says Akhilesh Tuteja, partner at KPMG. Bridging the gap will be key in turning a potential weakness into a strength.

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War of the machines: The opportunities in machine learning for businesses - Economic Times

If Robert Cardillo has his way, robots will perform 75 percent of the tasks currently done by American intelligence analysts who collect, analyze, and interpret images beamed from drones, satellites, and other feeds around the globe.

Cardillo, the director of the National Geospatial-Intelligence Agency, known by the acronym NGA, announced his push toward automation and artificial intelligence at a conference this week in San Antonio. The annual conference, hosted by the United States Geospatial Intelligence Foundation, brings together technologists, soldiers, and intelligence professionals to discuss national security threats, changes in technology, and data collection and processing.

Artificial intelligence is on the rise; former President Barack Obamas White House released a white paper on its potential future impacts in the final months of the administration. Police officers are using preliminary programs to predict the likelihood someone will commit a crime in a specific neighborhood based on crime statistics data. And companies like Amazon and Netflix use machine learning to calculate what movie you will want to watch or which book you may buy.

Yet this sort of automation is also seen as a threat to workers, who fear being put out of jobs, particularly in the private sector.

The fear that artificial intelligence will take over jobs, or fail catastrophically along the way, is palpable in the intelligence community as well, and Cardillo admitted that the workforce is skeptical, if not cynical or downright mad, about the prospect of automation intruding on their day-to-day lives, potentially replacing them.

The coming revolution in artificial intelligence has been hyped for years, often falling short of expectations. But if it does happen, analysts worry theyll become obsolete.

Cardillo, who called it a transforming opportunity for the profession, said hes working on showing the workforce that artificial intelligence is not all smoke and mirrors. The message hes sending to workers at the agency is that the goal of automation isnt to get rid of you its there to elevate you. Its about giving you a higher-level role to do the harder things.

In Cardillos eyes, the profession of geospatial intelligence monitoring and exploiting commercial and proprietary video and imagery feeds around the world is on the precipice of a data explosion similar to when the internet took off. At that point, the National Security Agency, which is responsible for collecting and analyzing digital communications, had to figure out ways to vacuum up and glean specific conclusions from an explosion of communications traveling back and forth on the web.

Just as the NSA employs algorithms to trawl through millions of messages, Cardillo wants machine learning to help with large volumes of imagery. Instead of analysts staring at millions of images of coastlines and beachfronts, computers could digitally pore over images, calculating baselines for elevation and other features of the landscape. NGAs goal is to establish a pattern of life for the surfaces of the Earth to be able to detect when that pattern changes, rather than looking for specific people or objects.

NGA is responsible for tracking potential threats, such as military testing sites in North Korea. When something at a site changes, like large groups of people or cars arriving, it may indicate preparations for a missile test. We dont have a higher priority, Cardillo told Foreign Policy. We put everything we can into North Korea.

But the number of sensors, images, and video feeds is exploding and will continue to grow in the coming years, he predicted. A significant chunk of the time, I will send [my employees] to a dark room to look at TV monitors to do national security essential work, Cardillo told reporters. But boy is it inefficient.

The agency is also turning to academia and the private sector for help. Cardillo hired Anthony Vinci, the founder and former CEO of Findyr, a company that crowdsources data from countries around the world, to head up the agencys machine-learning efforts within NGA.

Companies exhibiting at the conference were clearly on the artificial bandwagon, boasting flashy datasets and advanced algorithms. But not everyone was convinced relying on computers for the bulk of data crunching and analysis was such a great idea for intelligence work.

Justin Cleveland, a former intelligence official who works for the security company Authentic8 which created a secure browser called Silo that also allows intelligence professionals to disguise their cybertracks was skeptical of the automation boom. It can be helpful, he said in an interview at the conference. But you could have one bad algorithm and youre at war.

Taking humans out of the bulk of the process is bound to lead to errors. At the end of the day, you have to trust the person who wrote the algorithm over the analyst, Cleveland said.

Jimmy Comfort, a deputy director at the National Reconnaissance Office, was enthusiastic about certain applications for artificial intelligence in some areas like facial recognition. There are so many parallels with what the commercial guys are doing, he said in an interview.

But for his agency, which works mainly with satellites, the needs are different. Satellites take fewer images, from much farther away. Theres challenges for us doing that stuff from space, Comfort said.

Photo credit: CHIP SOMODEVILLA/Getty Images

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Artificial Intelligence Will Put Spies Out of Work, Too - Foreign Policy (blog)

By tracking cursor movement, lie detection becomes a game of cat and mouse.

DeanDrobot/iStock Photo

By Matthew HutsonJun. 9, 2017 , 3:30 PM

Every year, millions of people have their identities stolen. Theres no foolproof way to pinpoint fakers, but thanks to Italian researchers, investigators may soon have another tool at their disposala way to suss out frauds and other liars online with just a few clicks of a mouse.

Traditional methods of lie detection include face-to-face interviews and polygraphs that measure heart rate and skin conductance. But they cant be done remotely, or with large numbers of people. Researchers have come up with effective computer-based tests that measure reaction time in response to true and false personal information. For the tests to work, though, experimenters have to know the truth in advance.

To get around this obstacle, a team of Italian researchers has come up with an innovative way of figuring out the truth. They asked 20 volunteers to memorize the details of a fake identity and assume it as their own. The subjects then answered a set of yes-or-no questions using a computer, as did 20 truth-telling volunteers. Questions included things like: Is Giulia your name? and Were you born in 1995? Researchers recorded each answer and measured how the subjects mouse cursors moved, from the bottom middle of the screen to yes and no buttons in the top two corners.

Because liars can get to be as good as the rest of us at telling the truth, the researchers threw a wrench into their experiment. In addition to the 12 expected questions, they asked 12 unexpected questions based on the volunteers new identities. For example, they asked about a persons zodiac sign, based on their birth date. And they asked about the capital city of the subjects presumed region. A fraud might have memorized a fake birthday, but not known the corresponding zodiac sign, or been able to calculate it quickly enough. Weve found that if people rehearse lies, lying can be as easy as telling the truth, says Bruno Verschuere, a forensic psychologist at the University of Amsterdam who was not involved in the research, except when you ask unexpected questions.

The experimenters trained a computer to sort liars from truth tellers using the number of incorrect answers they gave. The teams four machine-learning algorithms ranged in accuracy from 77.5% to 85%. But when the researchers included features of the mouse pathssuch as deviation from a straight linein their training materials, computers were able to successfully pick out the liars 90% to 95% of the time, the researchers reported last month in PLOS ONE.

They also trained and tested the algorithms using only questions that the liars answered truthfully, such as whether they were Italian. The algorithms could still identify the fibbers with 77.5% to 80% accuracy. Jumping back and forth between telling the truth and lying seems to have a broad effect on peoples behavior, the scientists say. Having to tell a lie changes the way people tell the truth.

But would such a method work in the real world? Giuseppe Sartori, a forensic neuroscientist at the University of Paduain Italyand an author of the paper, says it could be used as a first screen to check peoples alibis in criminal investigations, verify identities online, or even cull terrorists from refugees at border checkpoints. It likely wont have the same accuracy it does in the lab, but he calls the study a good proof of concept.

Its a clever idea, says Giorgio Ganis, a cognitive neuroscientist at Plymouth University in the United Kingdom. But its not obvious that its going to be super useful. Ganis notes that in the real world, fraudsters would likely spend more time researching their backstories, making surprising questions harder to find. Youre going to catch the dumb criminals and dumb terrorists, he says, which is better than nothing, I guess. Sartori adds that even though impostors might learn their purported zodiac sign, other unexpected questions are practically unlimited. Do they know the cross streets of their purported home address? Do they know the layout of the restaurant where they say they were on the night of a crime? The study brings a whole new meaning to the game of cat and mouse.

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Are you lying about your identity? Artificial intelligence can tell by how you use your mouse - Science Magazine

The country is hoping to use the technology to transform a variety of industries, including manufacturing, health and transport.

Hundreds of gadgets and must-have electronic devices were on display at Shanghais Consumer Electronic Show, or CES Asia.

Among them was a driverless car powered by Baidus Project Apollo self-driving car platform. The tech giant is using machine-learning, a new step in artificial intelligence, to develop the technology.

Machine-learning allows computers to learn without being explicitly programmed.

The advance has gotten the attention of the Chinese government, which is spending big in AI. Some Chinese cities are pledging more than $2 billion dollars towards research and development.

Zha Hongbin is an AI researcher and professor at Beijings Peking University. He says machine learning has been the focus of many computer labs across China.

'Project Apollo' is Baidu's driverless car platform developed with AI

Our country is on the way to launch the project Artificial Intelligence 2.0, the launching of which would drive the development of machine learning," he said.

"The Chinese government attaches great importance to this area.

Mr Zha says China hopes to leap-frog the US and other western countries by vast and fast investment in the industry.

We still lag behind in some traditional industries," he said.

"When it comes to emerging industries we see an opportunity to be more advanced. And AI, we believe, would play a crucial role in this process.

The technology was behind the triumph of googles AI program AlphaGo in the highly complex Chinese board game Go last month. The program bested world champion Ke Jie in three games.

But the technology has more serious applications. Earlier this year tech giant Baidu worked with a charity to use facial recognition to help identify two missing persons, one who had been trafficked from home 28 years ago.

Beijing-based start-up Infervision uses sophisticated image recognition to detect lung cancer in CT scans.

Its not difficult for the human eye to detect late stage symptoms, but very easy for humans to miss small and early stage symptoms," Infervision chief executive Chen Kuan said.

"Using our technology we can actually detect the problems much earlier and I think thats the part in which we can save a lot of lives.

With their program installed in 20 hospitals across China, the start-up are now expanding to the US and Japan and developing programs to help doctors analyse images of the heart, brain and stomach.

Mr Chen says his goal isnt to replace doctors or radiologists, but allow them to work more efficiently.

Infervision uses AI technology to help scan lung images for cancer nodes

We can use deep-learning technology to remove parts in which they do repetitive and tedious work, he said.

But some believe job losses are inevitable. Technologist and venture capitalist Kai-fu Lee predicts AI will replace 50 per cent of all jobs over the next decade.

Zha Hongbin says manufacturing and transportation and will be especially affected, but changes will be good for the economy overall.

There will be some other industries emerging in markets like China and these emerging industries may employ more people, he said.

On a consumer-level, robot company Roobo believes most AI gadgets are in their infant-stages. Theyre produced three models which are intentionally designed to look cute.

If you design the interface to look too high-teach or intelligent the users may have too high expectations," Roobos chief technology officer Lei Yu said.

"We aim to eventually create a human-like interactive robot, but we cant achieve that yet.

Domgy, an interactive dog-like prototype, will go on the market for more than $1000 at the end of the year.

The price point is unaffordable for most in China, but the company hopeswithin five years the technology will have advanced enough to push prices down.

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China's bid to beat the world's artificial intelligence revolution - SBS

Artificial intelligence, AI, is all the rage these days -- analysts are proclaiming it will change the world as we know it, vendors are AI-washing their offerings, and business and IT leaders are taking a close look at what it can potentially deliver in terms of growth and efficiency.

For people at the front lines of the revolution, that means developing and honing skills in this new dark art. In this case, AI requires a blend of programming and data analytics skills, with the necessary business overlay.

In a recent report at the Dice site, William Terdoslavich explores some of the skills people will need to develop a repertoire in the AI space, noting that these skills are in high demand, especially with firms such as Google, IBM, Apple, Facebook, and Infosys absorbing all available talent.

Machine learning is the foundational skill for AI, and online courses such as those offered through Coursera offer some of the fundamental skills. Abdul Razack, senior VP and head of platforms at Infosys, notes that another way to develop AI expertise is to "take a statistical programmer and training them in data strategy, or teach more statistics to someone skilled in data processing."

Mathematical knowledge is also foundational, Terdoslavich adds, requiring a "solid grasp of probability, statistics, linear algebra, mathematical optimization--is crucial for those who wish to develop their own algorithms or modify existing ones to fit specific purposes and constraints."

Programs popular with AI developers include R, Python, Lisp, Prolog and Scala, Terdoslavich's article states. Older standbys -- such as C and C++ and Java -- are also being employed, depend upon applications and performance requirements. Platforms and toolsets such as TensorFlow also provide AI capabilities.

Ultimately, becoming adept in AI also requires a degree of a change in conceptual thinking as well, requiring deductive reasoning and decision-making.

AI skills -- again, which blend expertise n programming, data, and business development -- may continue to be in short supply, and David Kosbie, Andrew W. Moore, and Mark Stehlik sounded the alarm in a recent Harvard Business Review article, calling for an overhaul of computer science programs at all levels of education. AI is "not something a solitary genius cooks up in a garage," they state. "People who create this type of technology must be able to build teams, work in teams, and integrate solutions created by other teams."

This requires a change in the way programming is taught, they add. "We're too often teaching programming as if it were still the 90s, when the details of coding (think Visual Basic) were considered the heart of computer science. If you can slog through programming language details, you might learn something, but it's still a slog -- and it shouldn't be. Coding is a creative activity, so developing a programming course that is fun and exciting is eminently doable."

What's in demand right now in terms of AI skills? A perusal through current job listings yields the following examples of AI jobs:

Senior software developer - artificial intelligence and cognitive computing (insurance company): "Lead the application prototyping and development for on premise cognitive search and analytics technologies. Candidate should have experience with AI, machine learning, cognitive computing, text analytics, natural language processing, analytics and search technologies, vendors, platforms, APIs, microservices, enterprise architecture and security architecture."

Artificial intelligence engineer: (aerospace manufacturer): "Will join a fast-paced, rapid prototyping team focused on applied artificial intelligence. Basic qualifications: 5 years experience in C/C++ or Python. Algorithm experience. Experience with machine learning and digital signal processing (computer vision, software defined radio) libraries."

Artificial intelligence innovation leader (financial services firm): "Oversee strategic product development, product innovation and strategy efforts. Evaluate market and technology trends, key providers, legal/regulatory climate, product positioning, and pricing philosophy.... Work closely with IT to evaluate technology viability and application. Qualifications: 7+ years of senior level management experience, PhD/masters in computer science, AI, cognitive computing or related field."

Artificial intelligence/machine learning engineer (Silicon Valley startup): "Deal with large-scale data set with intensive hands-on code development. Collect, process and cleanse raw data from a wide variety of sources. Transform and convert unstructured data set into structured data products. Identify, generate, and select modeling features from various data set. Train and build machine learning models to meet product goals. Innovate new machine learning techniques to address product and business needs. Analyze and evaluate performance results from model execution." Qualifications: "Strong background and experience in machine learning and information retrieval. Must have experience managing end-to-end machine learning pipeline from data exploration, feature engineering, model building, performance evaluation, and online testing with TB to Petabyte-size datasets."

Read the rest here:

What it takes to build artificial intelligence skills - ZDNet

Analyzing a company's earnings call gives an investor a first hand heads-up on the company's latest status with regards to operational and financial health. Investors can read the transcript, look at the numbers, and draw their own conclusions.

In addition to the traditional approach of evaluating an earnings call, we used our Artificial Intelligence engine to objectively analyze a call transcript. The purpose of this exercise is to acquire additional insights directly from the company's perspective. This write-up focuses on the Executive Statement from the IBM (NYSE:IBM) 2017 Q1 Earnings Call.

The following is a summary of findings:

Analytics with Artificial Intelligence

Our AI Analytics is based on symbolic logic and propositional calculus. In other words, our algorithm discovers symbols that represent some level of importance based on propositional logic to drive a causational model. The causational model seeks out supporting context surrounding these situations. Thus, for each of the points, we expect AI to tell us the rationale.

In a nutshell, the AI part of the analysis is to read the transcript like a human researcher and bring out positive points, negative points, and points with both positive and negative aspects. It does so in an objective way using Meta-Vision.

Our AI analysis of the earnings call Executive Statement resulted in the following Meta-Vision:

Meta-Vision Legend:

Our AI engine discovers important points we call 'Meta-Objects'. There are two type of Meta Objects, namely, Machine Generated Hashtag (or MGH) nodes and Supporting Fact (or SF) nodes. MGH nodes are important points discovered by CIF from the given dataset. SF nodes are the text that is being analyzed. 'Meta-Vision' is the topological mapping of Meta-Objects across a quadrant chart by semantics, context, and polarity. The quadrant chart connects Meta-Objects (MGH and SF nodes) by edges to depict their respective relationships. Clicking on a node opens a new window showing corresponding context for that node. The North-East "NE" quadrant is called the "common-positive quadrant." The North-West "NW" quadrant is called the "common-negative quadrant." The South-West "SW" quadrant is the "negative quadrant." The South-East "SE" quadrant is the "positive quadrant." The name of each quadrant denotes the connotation (common, negative, positive). Placement of nodes are determined by the AI. Machine generated hashtag nodes are labeled. The relative location from the X-axis denotes the strength of a MGH node. The closer the FN nodes are to the center, the higher the number of MGH nodes that it supports.

For each of the important points (MGH node), the co-ordinate indicates the connotation. Clicking a MGH will bring out all the corresponding quotes in verbatim from the transcript (supporting facts and context). MGH nodes are also connected to fact nodes. Each Fact node represents the excerpts from the original document. Clicking a fact node will bring out the semantic and sentiment analytics on that excerpt.

In summary, without any human interaction or influence, our AI algorithm has determined that the following points, represented by machine generated hashtags, are negatively stated in the earnings call: #Income, #GBS, #Earning, #Workforce

Our AI algorithm determined that the following points, represented by machine generated hashtags, are positively stated in the earning call: #Cloud, #Solutions, #Digital, #Profit, #Investment, #IBM

Our AI algorithm determined two points carried a negative connotation, but also has positive aspects. They are: #Software, #Track

Our AI algorithm determined that the following points contained both positive and negative supporting facts, while the positive supporting facts are dominant: #Margin, #Client

Our AI algorithm determined that the following points contained both negative and positive supporting facts, while the negative supporting facts are dominant: #Performance, #Revenue

Evaluating the Executive Statement with Meta-Vision

Based on our examination, we identified strategic points and corresponding supporting facts. We did so with the following agendas in mind:

The following are points (MGH nodes) that we picked out are based on the above criteria:

#income #workforce

#gbs

#cloud

#ibm

#margin, #solutions, #profit

#clients

Deriving Insights through Bionic Fusion

While the details of the technology behind the analysis is beyond of scope of this article, the general concept is not difficult to understand. The idea is to equip a software system with the ability to master a language, such as English, to the equivalent of a graduate student or researcher who can learn a core subject from a lecture or research medium. In this scenario, the medium uses English to introduce new subjects. In the process of knowledge transfer, the medium draws relationships between subjects and expresses the properties of the underlying context. The researcher, using English as a medium, can learn any subject and acquire new knowledge by listening to lectures. In a similar manner, the software system uses visual charts to depict the discovered subjects, relationships, underlying context, properties, and references to source documents. When a user navigates through these properties, together with human thinking, it forms a bond of bionic fusion which enables the user to gain insights by drawing inference from these visuals.

The AI algorithm did the work of identifying important points, connotation, and supporting facts. We examined each point and supporting fact to draw inference into perceived strengths and weaknesses. To corroborate our findings, we also referred to our enterprise data lake for business intelligence around competitive marketspace and external market forces.

RE: GBS, Strategic Imperatives

If management saw growth in its Strategic Imperatives, IBM would need the following:

This needs upfront investment, a substantial increase in human capital, and a faster time to market with industry-specific vertical applications. This proposition is contradicted by the decline in Global Business Services (or GBS). If management was dedicated to building a backlog and pipeline in its GBS unit, the subsequent rebalance of workforce should result in an increase in expense. Judging from the continuing rebalancing of workforce in the negative column, and the need to build industry specific solutions, GBS will have problems with scale. Customers cannot put their business on hold and will seek for alternative competitive solutions in the marketplace such as open source or off-the-shelf solutions. Consequently, we do not believe that management is confident in GBS pipeline growth.

RE: Cloud

IBM is transforming their business into a 'data and cloud first' company. The super set of cloud business consists of private cloud (enterprise cloud), public cloud, and hybrid cloud. IBM's cloud is not a public cloud like Amazon (NASDAQ:AMZN)'s AWS offering. IBM only focuses on enterprise. The public cloud space has a market cap that is projected to exceed $500 billion by 2020. IBM's Executive Statement did not reflect any initiative that would position IBM for a share of this huge market. The enterprise cloud space has major competitors such as HP (NYSE:HPE), Microsoft (NASDAQ:MSFT), and Google (NASDAQ:GOOG). Moreover, IBM's enterprise cloud is a service that will compete with IBM's legacy mainframe business for the same customer IT budget. IBM recognizes that this shift will require a level of investment a longer return profile which is already being reflected in their margins and will require continued investment.

RE: Cognitive

Cognitive is industry-specific. It will cost substantial time and additional investment in building each of the vertical problem domains. Artificial Intelligence is becoming a crowded market. IBM will have to compete with new startups. Time, cost and efficiency will weigh against IBM just like its legacy Personal Computing and server business. Technology is changing at a fast pace; custom-built solutions that takes years to materialize will face obsolescence before it is put to use.

Conclusion:

Products and services that make up the Strategic Imperatives are part of the "red-ocean" in a crowded market. If Strategic Imperatives as identified by IBM is its main turnaround strategy, it is going to face a lot of competition. Based on the Meta-Vision analysis of IBM's 2017 Q1 earnings call, we do not see any counter initiatives that will improve IBM's outlook in near-term.

Additional Notes - Process of Analysis:

Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.

I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

Additional disclosure: I am neither a certified investment advisor nor a certified tax professional. The data presented here is for informational purposes only and is not meant to serve as a buy or sell recommendation. The analytic tools used in this analysis are products of SiteFocus.

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An Artificial Intelligence Retrospective Analysis Of IBM 2017 Q1 Earnings Call - Seeking Alpha

Forbes

How Will Artificial Intelligence Change Healthcare? Forbes How will AI change healthcare? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world. Answer by Abdul Hamid Halabi, Business Lead, Healthcare & Life Sciences at ... and more »

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How Will Artificial Intelligence Change Healthcare? - Forbes