Daily Archives: March 9, 2017

The same artificial intelligence that may soon drive your new car is being adapted to help drive interventional radiology care for patients.

Researchers at the University of California, Los Angeles (UCLA), have used advanced artificial intelligence, also called machine learning, to create a chatbot or Virtual Interventional Radiologist (VIR).

This device communicates automatically with a patients physicians and can quickly offer evidence-based answers to frequently asked questions.

The scientists will present their research today at the Society of Interventional Radiologys 2017 annual scientific meeting in Washington, D.C.

This breakthrough will allow clinicians to give patients real-time information on interventional radiology procedures as well as planning the next step of their treatment.

Dr. Edward W. Lee, assistant professor of radiology at UCLAs David Geffen School of Medicine, and one of the authors of the study, said he and his colleagues theorized they could use artificial intelligence in low-cost, automated ways to improve patient care.

The fundamental technology that has made self-driving cars possible is deep learning, a type of artificial intelligence modeled after the connections in the human brain, explained Dr. Kevin Seals, resident physician in diagnostic radiology at UCLA Health, and a study co-author, said in a Healthline interview.

Seals, who programmed the VIR, said advanced computers and the human brain have a number of similarities.

Using deep learning, computers are now essentially as good as humans at identifying particular objects, making it possible for self-driving cars to see and appropriately navigate their environment, he said.

This same technology can allow computers to understand complex text inputs such as medical questions from healthcare professionals, he added. By implementing deep learning using the IBM Watson cognitive technology and Natural Language Processing, we are able to make our virtual interventional radiologist smart enough to understand questions from physicians and respond in a smart, useful way.

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Think of it as an initial, superfast layer of information gathering that can be used prior to taking the time to contact an actual human diagnostic or interventional radiologist, Seals said.

The user simply texts a question to the virtual radiologist, which in many cases provides an excellent, evidence-based response more or lessinstantaneously, he said.

He noted that if the patient doesnt receive a helpful response, they are rapidly referred to a human radiologist.

Tools such as our chatbot are particularly important in the current clinical environment, which focuses on quality metrics and follows evidence-based clinical guidelines that are proven to help patients, he said.

Seals said a team of academic radiologists curated the information provided in the application from the radiology literature, and it is rigorously scientific and evidence-based.

We hope that using the application will encourage cutting-edge patient management that results in improved patient care and significantly benefits our patients, he added.

It can be thought of as texting with a virtual representation of a human radiologist that offers a significant chunk of the functionality of speaking with an actual human radiologist, Seals said.

When the non-radiologist clinician texts a question to the VIR, deep learning is used to understand that message and respond in an intelligent manner.

We get a lot of questions that are fairly readily automated, Seals said. Such as I am worried that my patient has a blood clot in their lungs. What is the best type of imaging to perform to make the diagnosis? The chatbot can respond to questions like this in a supersmart, evidence-based way.

Sample responses, he said, can include instructive images (for example, a flowchart that shows a clinical algorithm), response text messages, and subprograms within the application such as a calculator to determine a patients Wells score, a metric doctors use to guide clinical management.

The VIR application resembles an online customer service chat.

To create a crucial foundation of knowledge, the researchers fed the app more than 2,000 data points that simulated the common inquiries interventional radiologists receive when they meet with patients.

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When a referring clinician asks a question, the extensive knowledge base of the app allows it to respond instantly with the best answer.

The various forms of responses can include websites, infographics, and custom programs.

If the VIR determines that an answer requires a human response, the program will provide contact information for a human interventional radiologist.

The app learns as clinicians use it, and each scenario teaches the VIR to become increasingly smarter and more powerful, Seals said.

The nature of chatbot communications should protect patient privacy.

Confidentiality is critically important in the world of modern technology and something we take very seriously, Seals said.

He added that the application was created and programmed by physicians with extensive HIPAA (Health Insurance Portability and Accountability Act of 1996) training.

We are able to avoid these issues because users ask questions in a general and anonymous manner, Seals said. Protected health information is never needed to use the application, nor is it relevant to its function.

All users professional healthcare providers such as physicians and nurses must agree to not include any specific protected patient information in their texts to the chatbot, he added.

None of the diverse functionality within the application requires specific patient information, Seals said.

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This new technology represents the fastest and easiest way for clinicians to get the information they need in the hospital, starting with radiology and eventually expanding to other specialties such as neurosurgery and cardiology, Seals said.

Our technology can power any type of physician chatbot, he explained. Currently, there are information silos of sorts that exist between various specialists in the hospital, and there is no good tool for rapidly sharing information between these silos. It is often slow and difficult to get a busy radiologist on the phone, which inconveniences clinicians and delays patient care.

Other clinicians at the UCLA David Geffen School of Medicine are testing the chatbot, and Seals and Lee say their technology is fully functional now.

We are refining it and perfecting it so it can thrive in a wide release, Seals said.

Seals engineering and software background allowed him to perform the necessary programming for the as-yet unfunded research project. He said he and his colleagues will seek funding as they expand.

This breakthrough technology will debut soon.

The VIR will be made available in about one month to all clinicians at the UCLA Ronald Reagan Medical Center. Further use at UCLA will help the team to refine the chatbot for wider release.

The VIR could also become a free app.

We are exploring potential models for releasing the application, Seals said. It may very well be a free tool we release to assist our clinician colleagues, as we are academic radiologists focused on sharing knowledge and improving clinical medicine.

The researchers described the importance of the VIR in a summary of their findings: Improved artificial intelligence through deep learning has the potential to fundamentally transform our society, from automated image analysis to the creation of self-driving cars.

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Artificial Intelligence for Cars May Drive Future of Healthcare - Healthline

IBM CEO Ginni Rometty told investors that her company is emerging as a leader in cognitive computing. (IBM)

IBM (IBM) is an attractive turnaround story with improved fundamental trends, says a Drexel Burnham analyst who reiterated a buy rating and raised his price target on the computer giant.

The buy rating by Drexel Burnham analyst Brian White follows a day of briefings that IBM presented to investors at its annual Investor Briefing conference that ended Tuesday.

"We believe IBM has further upside potential as the fruits of the company's labor around its strategic imperatives are better appreciated and more investors warm up to the stock," White wrote in a research note. Along with his buy rating, White raised his price target on IBM to 215, from 186.

IBM stock ended the regular trading session at179.45, down fractionally on the stock market today. It's currently trading near a 29-month high.

The investor's day events included a presentation by IBM Chief Executive Ginni Rometty, who said the company has reached an important moment with a solid foundation and is emerging as a leader in cognitive computing with its Watson computing platform and cloud services.

Announcements from the investor briefing included IBM and Salesforce.com (CRM) agreeing to a strategic partnership focused on artificial intelligence and supported by IBM's Watson computer and the Einstein computing platform by Salesforce.com.

Salesforce and IBM will combine their two AI offerings but will also continue to sell the combined offering under two brands. Salesforce and IBM said they would "seamlessly connect" their AI offerings "to enable an entirely new level of intelligent customer engagement across sales, service, marketing, commerce and more."

Salesforce stock finished at83.48, up 0.6%.

Decades of research and billions of dollars have poured into developingartificial intelligence, which has crossedover from science fiction to game-show novelty to the cusp of widespread business applications. IBM has said Watson represents a new era of computing.

IBD'S TAKE: After six consecutive quarters of declining quarterly earnings at IBM,growth may be on the mend. IBM reported fourth-quarter earnings after the market close Jan. 19 that beat on the top and bottom lines for the fifth straight quarter.

"We believe IBM is furthest ahead in the cognitive computing movement and we believe the Salesforce partnership is only the beginning of more deals in the coming years," White wrote.

Other companies investing heavily in AI include Google parent Alphabet (GOOGL) and graphics chip company Nvidia (NVDA).

Alphabet has used AI to enhance Google search abilities, improve voice recognition and to derive more data from images and video.

Nvidia has developed chip technology for AI platforms used in autonomous driving features, and to enhance how a driver and car communicate.

Not everyone is a bull on the IBM train. Credit Suisse analyst Kulbinder Garcha, has an underperform rating on IBM and price target of 110. Garcha, in a research note, said IBM remains in a multiyear turnaround.

"We believe it will take multiple years for faster growing segments such as the Cognitive Solutions segment and Cloud to offset the decline in the core business," Garcha wrote.

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IBM Rated Buy On 'Upside Potential,' Artificial Intelligence Move - Investor's Business Daily

SAN FRANCISCO : Google is betting big on its dominance in machine learning and artificial intelligence to break into the cloud market, a message that was the underlying theme on the first day of the technology giant's cloud conference that began here on Wednesday.

It also made a slew of announcements further strengthening its place as the leader in machine learning and artificial intelligence platforms.

We put $30 billion in the Google Cloud Platform, said Eric Schmidt, chairman of Googles parent company Alphabet. He added that big data, or large data sets that are analysed to reveal patterns through machine learning and artificial intelligence, is so powerful that nation states will fight over it.

Google announced big names, such as HSBC, Colgate-Palmolive, the Home Depot, SAP, Disney, Verizon and Ebay as customers, most of who have large data sets to the tune of billions of records.

Google currently lags in the cloud market, with Amazon Web Services and Microsoft's Azure taking the lead with big customers, but the technology giant aims to change that through the Google Cloud Platform (GCP) which allows customers to leverage its machine learning and artificial intelligence capabilities.

Google Cloud's vice president Diane Greene led the agenda on the opening day, along with CEO Sundar Pichai, Fei Fei Li, the chief scientist, cloud AI and ML, Eric Schmidt.

The GCP lets enterprises host applications and websites, store data, and analyze data on Google's scalable infrastructure. Added on top of that is Googles machine learning capability which is widely acknowledged as being the best in the industry.

Machine learning is a phenomenal tool for enterprises to get insights like never before, said Greene at a post keynote press conference.

Artificial Intelligence, or building applications that mimic human-like behaviour, making them more intuitive and useful to the end consumer, is fast becoming the battleground for all major cloud providers, as they take a step further from providing just storage capability to their clients.

Research firm IDC said last year that widespread adoption of cognitive systems and AI across a broad range of industries will drive their worldwide revenues from nearly $8 billion in 2016 to more than $47 billion in 2020. Last year, banking, retail, healthcare and discrete manufacturing were the largest spenders in AI. In the future, education and process manufacturing are expected to drive more revenues.

ET reported last week that the India arm of Amazon Web Services (AWS) is looking to add AI services such as speech recognition, text-to-voice services, visual search and image analysis, to its base infrastructure on which startups, enterprises and developers can build their products.

Google, with its deep learning capability through its Brain project as well as the work done by Li, has an edge over its competitors in understanding and crunching large data sets, as well as understanding artificial intelligence.

Li, whose work in visual search is well recognised, built on the theme of "democratizing AI", and said Google wants to take it to developers, people and enterprises.

Describing video as the dark matter of digital, Li launched the Google video API for Google Cloud, which accurately identifies things and places in videos. For instance, in a medical procedure video, if a user wants to search for specific body parts, they will be able to do it just by searching for the name of the part instead of having to go through the entire video.

Google also announced the acquisition of Kaggle, a San Francisco-based data science startup that runs programming contests and competitions for machine learning projects. Google did not disclose the value of the acquisition, but said Kaggle would continue to operate as an independent brand for a while.

Some of the Google announced on Wednesday also use AWS and Microsoft clouds, in what they described as having a multi-cloud strategy. We want all players in the cloud market to compete against each other, said Paul Gaffney, senior vice president at The Home Depot, summing up what the cloud market is already looking like.

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Google bets big on artificial intelligence to make a cloud push for enterprises - Economic Times

Mumbai: As pro vice-chancellor (product and partner development) of the University of Sunderland in the UK, Prof. John MacIntyres brief includes covering research, innovation, knowledge exchange, employer engagement and regional economy. Since 1996, MacIntyre has also been the editor-in-chief of Neural Computing and Applicationsan international scientific peer- reviewed journal published by Springer Verlag.

In an interview, he talks about why artificial intelligence (AI) needs to be looked at more positively and how AI can contribute to society. MacIntyre will also address EmTech India 2017an emerging tech conference organized by Mint and MIT Technology Reviewon 9 March in New Delhi. Edited excerpts:

You completed your PhD in applied AI, focussing on the use of neural networks in predictive maintenance. What prompted you to do this research and what were your research findings?

When I worked in the Middle East, I taught myself programming and did a range of jobs and tasks to build my skill sets. I ended up managing teams and wanted to further my career, but also realized that I needed formal qualifications to do that. So, I returned to the UK, and took a full-time job working night shifts, to allow me to study full-time during the day.

The University of Sunderland had a programme of Combined Sciences that allowed you to take a major and minor optionso I majored in computer science, and my minor choice was physiologywhich I chose simply out of personal interest. As it happened, it became very relevant as I then embarked on my doctoral work.

Having achieved a First Class Honours degree, I was offered the chance to do a PhDand the most interesting option was a programme of research looking at how to improve the performance and reduce costs of a power generation plant through predictive maintenance and condition monitoring. The sponsor company was National Power, and I liked the idea of applying my knowledge in computer science and engineering to a specific industrial problem, and coming up with new ideas.

My physiology minor ended up being relevant because of the choice of using neural networks as a model or technique for pattern recognition and classification, in the face of very noisy and sometimes incomplete data, to provide diagnostics and prognostics for engineers to use in making decisions about maintaining the ancillary plant in power generation stations.

By the time I completed my PhD, we had saved literally millions of pounds for the company, through elimination of catastrophic failures, reduced downtime of generating plant, and reduced costs.

The study of neural networks does involve an interdisciplinary approach. Please elaborate.

Applications of neural networks (and the associated natural computational techniques, such as genetic algorithms) are incredibly varied and diverse. This is because the range of techniques can be applied, appropriately, to a wide range of problem typesclassification, pattern recognition, optimization and prediction, to name only a fewin an even wider range of sectors and applications e.g. medical, industrial, financial, commercial, geophysical, and so on.

This means that collaborative ventures, where expertise from a range of fields is brought to bear on applying the techniques to help solve a problem or create a solution (not necessarily a perfect solution, but at least an advance on current technology) are becoming commonplace.

Doctors, engineers, bankers, geologists, physicists, metallurgists and computer scientists will all work together in various project teams to focus their collective expertise on applying AI techniques to create advances in knowledge and technology. I see this as the way forward and it is always refreshing to see how the blend of such disciplinary expertise creates a new dynamic to tackle difficult problems.

While there are those who believe in the potential of AI and its applications, a sizeable number of people including Stephen Hawking, Bill Gates and Elon Musk have expressed fears that AI-powered machines could rule over humans? Whats your take on this subject?

This is a major problem and encompasses some really big issues, including understanding, ignorance, focus and ethics. AI is already all around us, sometimes in very visible ways (e.g. Siri) but often in very invisible ways (linked to Internet profiling, banking algorithms, even embedded AI in cameras and washing machines).

These applications would generally be seen as positive, supporting humans in their modern, everyday lives. And yet, still, AI is perceived very negatively by many in society who dont understand what AI really is, and what it means to them.

As editor-in-chief of the scientific journal Neural Computing and Applications, published by Springer Verlag, I see thousands of scientific papers each year, from all around the world, advancing AI techniques and applicationsall of which, I would say, are intended to be positive contributions to society.

The problem is that the general public only see, and quite understandably, take their information from what the media, and in particular, film and TV, put before them. And because that is dominated by negative stories about AI taking over the world, eliminating humans (literally or metaphorically), and rendering humanity obsolete, its hardly surprising that most people have a pretty negative view of AI.

I believe the scientific and technical community has a responsibility to counter this negative with good news about AI, and to make it understandable, accessible, and therefore less frightening to society.

Tell us something about the work that the University of Sunderland does with its Institute of Automotive Manufacturing and Advanced Practice (AMAP). Do you believe that electric vehicles and connected cars will be the normal by 2025?

Connected cars are already here!

Most new generation vehicles are already IP-enabled devices with sophisticated interfaces, connecting them to the Internet. The next few years will see more developments in how vehicles connect to the environment, for example, the Connected Car programme of Hitachi Data Systems is driving towards the CFX conceptwhere the car can connect to any other Internet-enabled device.

The major developments are linked to the development of driverless carsautonomous vehicles, in effect.

There are many, many difficult issues to resolve before driverless cars will be the normand I think that is likely to be decades away. Electric (and other alternatively-fuelled) vehicles are already commonplace, but I dont think they will have completely replaced the internal combustion (IC) engine by 2025.

It seems to me that we will see, over say the next 20 years, a multi-faceted strategy of development, with even more efficient and clean IC engines being developed alongside improvements in battery technology and range for electric vehicles, and hydrogen and other alternatively-fuelled vehicles also being developed.

Right now, it is impossible to say which will become the dominant technology, or when.

First Published: Thu, Mar 09 2017. 05 02 AM IST

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Artificial intelligence is already all around us: John MacIntyre - Livemint