Monthly Archives: May 2017

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You Don't Need To Be A Data Scientist To Implement AI With Bonsai Forbes Bonsai, an artificial intelligence startup based in Berkeley, California, aims to democratize AI by making the technology accessible to business decision makers. It is abstracting the complexity involved in implementing frameworks such as TensorFlow ...

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You Don't Need To Be A Data Scientist To Implement AI With Bonsai - Forbes

Google this week held its developer conference for 2017, where it teased some of the brand new features coming to its products and services.

What we saw on stage was undeniably impressive, but one of the demonstrations in particular was frighteningly so.

Google Lens will give the company greater insight into our daily lives than ever before.

It was one of the first things to be revealed at the conference, and few people expected it to steal the entire show, as it ended up doing.

Lens isnt available to consumers yet, but when it does arrive, it should prove seriously useful and, thus, incredibly popular.

It uses machine-learning to identify real-world objects through your phones camera, but thats just the start of the story.

It can also analyse everything it sees, understand the context, work out where you are, and figure out what you want to do.

As shown by Google, Lens can use optical character recognition to take the username and password from a Wi-Fi router, and instantly connect your phone to that network.

It can also bring up restaurant reviews and details, using GPS location data to instantly work out which branch youre considering going to.

All you need to do is point your camera in the right direction.

In an AI-first world, we are rethinking all our products, said Google CEO Sundar Pichai, who announced the companys plans to use machine-learning to improve everything it does.

Google says Lens is coming to both Photos and Assistant.

The former, incidentally, will use machine-learning to analyse your pictures more thoroughly than ever. As well as editing them and recognising the people in them, it will prompt you to send the right photos to the right people, and invite your contacts to send pictures of you, to you.

Assistant, meanwhile, has just been rolled out to Apples App Store. Photos is already available on iOS.

The company is quietly transforming your camera into a search engine.

While thats arguably the next natural step forward, the amount of data both iPhone and Android users will feed straight to the company will be staggering.

Google Glassfailed to take off because people outside the tech community thought it was horribly creepy.Lens is aiming to bea socially-acceptable version of it.

Google doesnt have the best of reputations when it comes to the privacy of its users, and the thought of the company not only being able to see everything you see, but to understand it too, won't sit comfortably with everyone.

Google's vision of the future looks incredible, but the fear is that all of that convenience will come at a huge price.

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Google's AI future: So impressive it's scary - The Independent

The days of claiming artificial intelligence as a feature that set one startup or company apart from the others are over. These days, one would be hard-pressed to find any technology company attracting venture funding or partnerships that doesnt posit to use some form of machine learning. But for companies trying to innovate in healthcare using artificial intelligence, the stakes are considerably higher, meaning the hype surrounding the buzzword can be deflated far more quickly than in some other industry, where a mistaken algorithm doesnt mean the difference between life and death.

Over the past five years, the number of digital health companies employing some form of artificial intelligence has dramatically increased. CB Insights tracked 100 AI-focused healthcare companies just this year, and noted 50 had raised their first equity rounds since January 2015. Deals in the space grew from under 20 in 2012 to nearly 70 in 2016. A recent survey found that more than half of hospitals plan to adopt artificial intelligence within 5 years, and 35 percent plan to do so within two years. In Boston, Partners HealthCare just announced a 10-year collaboration with GE Healthcare to integrate deep learning technology across their network. The applications for AI go far beyond just improving clinician workflow and processing claims faster.

The problem we are trying to solve is one of productivity, Andy Slavitt, the former acting administrator of the Centers for Medicare and Medicaid Services said during the Light Forum, a two-day conference that brought together CEOs, healthcare IT experts, policymakers and physicians at Stanford University last week. We need to be taking care of more people with less resources, but if we chase too many problems and business models or try to invent new gadgets, thats not going to change productivity. Thats where data and machine learning capabilities will come in."

Respondents to the hospital survey said the technology could have the most impact on population health, clinical decision support, diagnostic tools and precision medicine. Even drug development, real world evidence collection and clinical trials could be faster, cheaper and more accurate with AI. But the time to put all of our faith in AI is still not here. The human brain is a really strong prior on what makes sense, Andrew Maas, chief scientist and cofounder of Roam Analytics said during the Light Forum. Computers are powerful on assessing, but not on the level of reliability you will trust soon.

How do we get there?

So everybody wants it, but just how soon will we see the purported transformation of healthcare from machine learning? Lately, weve seen it in everything from the most straightforward app to the most complex diagnostic tasks, coming in the form of natural language processing or image recognition to powerful algorithms crunching databases made up of decades of medical research.

Like any other technology in healthcare, AI cant be brought in without a mountain of extra challenges including regulatory barriers, interoperability with legacy hospital IT systems, and serious limitations on access to crucial medical data needed to build powerful health-focused algorithms in the first place. But thats not stopping innovation, albeit cautious innovation, and digital health stakeholders are realizing that unlocking AIs true full potential requires strategic partnerships, quality data, and a sober understanding of statistics.

As the understanding of AI in healthcare matures, the biggest names in technology arent shying away from the mountainous challenges that come with innovating in the industry, like regulatory barriers, legal access to quality data and the constant issue of lack of interoperability. Just this week, Google announced it has built upon its tried and true consumer-level machine learning capabilities into healthcare. Google Brain, the companys research team, worked with the likes of Stanford, University of California San Francisco to acquire de-identified data from millions of patients.

Its more than that, as Google CEO Sundar Pichai explained at the tech giants Google I/O developer event last week. Last year, they launched the Tensor computing centers, which the company describes as AI-first data centers.

At Google, we are bringing all of our AI efforts together under Google.ai. Its a collection of efforts and teams across the company focused on bringing the benefits of AI to everyone, Pichai said. Google.ai will focus on three areas: Research, Tools and Infrastructure, and Applied AI.

In November, Google researchers published a paper in JAMA showing that Google's deep learning algorithm, trained on a large data set of fundus images, can detect diabetic retinopathy with better than 90 percent accuracy. Pichai said another area they are looking to apply AI is pathology.

This is a large data problem, but one which machine learning is uniquely equipped to solve, he said. So we built neural nets to detect cancer spreading to adjacent lymph nodes. Its early days but our neural nets show a much higher degree of accuracy: 89 percent, compared to 73 percent. There are important caveats we also have higher numbers of false positives but already getting this in the hands of pathologists, they can improve diagnosis.

Another example is Apples recent acquisition of AI company Lattice, which has a background in developing algorithms for healthcare applications.

Microsoft, too, is wading into the space. Just a couple of months ago, the company launched the Healthcare NExT initiative, which brings together artificial intelligence, cloud computing, research and industry partnerships. The initiative includes projects focused on genomics analysis and health chatbot technology, and a partnership with the University of Pittsburgh Medical Center. A couple of weeks ago, Microsoft partnered with data connectivity platform provider Validic to add patient engagement to their HealthVault Insights research project.

Weve seen AI in various forms in lots of startups, too, from Ginger.ios behavioral health monitoring and analytics platform Senselys virtual assistants to apps and wearables from companies like Ava which just released research with the University of Zurich and Clue, to predict fertility windows. Others, like the recently-launched Buoy Health, have created medical specific search engines. Buoy sources from over 18,000 clinical papers, covering 5 million patients and spanning 1,700 conditions. Beyond a symptom checker, Buoy starts by asking age, sex, and symptoms, then measures against the proprietary and granular data to decide which questions to ask next. Over about two to three minutes, Buoys questions narrow down to get more and more specific before offering individuals a list of possible conditions, along with options for what to do next.

Another promising area is medical imaging. In November, Israel-based Zebra Medical Vision, a machine-learning imaging analytics company, announced the launch of new platform that allows people upload and receive analysis of their medical scans from anywhere with an internet connection. Zebra launched in 2014 with a mission to teach computers to automatically analyze medical images and diagnose various conditions, from bone health to cardiovascular disease. The company has steadily built up an imaging database, which they are combining with deep learning techniques in order to developing algorithms to automatically detect and diagnose medical conditions. Another Israeli company with a similar offering is AiDoc, which just raised $7 million.

But no matter how big and powerful the technology company may be, the availability of patient data is what makes the difference between a buzzword or an algorithm that can diagnose or predict outcomes. Thats why many companies are in the training stage.

As Joe Lonsdale, CEO of venture capital firm 8VC said during the Light Forum at Stanford, The hard part is creating the data in the first place.

Dr. Maya Peterson, a professor of biostatistics at the University of California Berkeley School of Public Health, offered an even more sober view.

Relationships [between data] in the real world are complex, and we dont fully understand them, she said during HIMSS' Big Data and Healthcare Analytics Forum in San Francisco this week. And machine learning is overly ambitious in a way, as we are going into more complex questions. That isnt a good thing.

A good algorithm is hard to build

Machines can only learn from the data provided them, so researchers, engineers and entrepreneurs alike are busy assembling larger and higher quality databases.

Last month, Alphabet-owned Verily launched the Project Baseline Study, a collaborative effort with Stanford Medicine and Duke University School of Medicine to amass a large collection of broad phenotypic health data in hopes of developing a well-defined reference of human health. Project Baseline aims to gather data from around 10,000 participants, each of whom will be followed for four years, and will use that data to develop a baseline map of human health as well as to gain insights about the transitions from health to disease. Data will come in a number of forms, including clinical, imaging, self-reported, behavioral, and that from sensors and biospecimen samples. The studys data repository will be built on Google computing infrastructure and hosted on Google Cloud Platform.

If the government did data quality and data sharing initiatives, it would be a lot different, Andrew Maas, chief scientist at Roam Analytics (a San Francisco-based machine learning analytics platform provider focused on life sciences) said at the Light Forum. If the private sector wants to do that, and gather data in abundance, thats great. Give us that data and well be back and have something amazing in a year. But if data is not collected because people are scared, we cant do anything.

The availability of patient data and computing power means the difference between promises and actual impact. That brings us to IBM Watson Health, which has amassed giant amounts of data via numerous partnerships, teaching the cognitive computing models it claims will unlock vast amounts of insights on patient health. As actual evidence are yet to be fully realized, public opinion on IBM Watson is split. Some think it is the granddaddy of machine learning.

During the Light Forum, Chris Potts, Stanford Universitys director of Linguistics and Computer Science as well as the chief scientist at Roam Analytics, said Watson is arguably the most promising in health. Others arent so sure Social Capital CEO Chamath Palihapitiya called it a joke. But, as evidenced by the many collaborations we have reported on, that doesnt seem to be hindering the companys ability to take up new partners. Just last week, they joined MAP Health Management to bring their machine learning capabilities to substance abuse disorder treatment, and the research arm of IBM is working with Sutter Health to develop methods to predict heart failure based on under-utilized EHR data.

IBM Watson actually got its start in 2011, when the machine won a game of Jeopardy, inspiring the company to get to work putting the technology to use.

We had to train the technology for the medical domain, and there are many complexities there it varies by specialty, and thats all different in different parts of the world. We had to train system to understand language of medicine, Shiva Kumar, Watson Healths vice president and chief strategy officer said at the Light Forum. The first step is natural language processing. Can you know enough to start deriving insights? Can you do that at the point that you engage in dialogue to come up with best possible answers? Talk to patient, go a step further, assimilate, continue moving on.

To do that, IBM Watson must tackle the problem of unstructured data, Kumar explained.

We tend to use word cognitive computing, because it goes beyond machine learning and deep learning. Being able to derive insights, being able to integrate, and learn. Healthcare is unique; its highly regulated, and has a ton of data it cant use. And there are many silos, he said. So its a place where a lot of technology can improve it. But at the end of day, success is determined by practitioners.

How to move forward

Many experts predict AI will hit healthcare in waves Allscripts Analytics Chief Medical Officer Dr. Fatima Paruk told Beckers Hospital Review said she foresees the first applications in care management of chronic diseases, followed by developments that leverage the increasing availability of patient-centered health data along with environmental or socioeconomic factors. Next, genetic information, integrated into care management, will make precision medicine a reality.

Some of the areas where AI could make the biggest impact are those already notoriously late to the technology game: Pharmaceutical companies. But thats starting to change. During the Light Forum, Jeff Kindler, partner at Lux Capital and former chairman and CEO of Pfizer, called pharma the classic example of innovators dilemma, due to the fact that they have never been in a tight enough financial position to be required to shift their business model. But seeing the potential of AI to speed up the process is too hard to pass up, although it will take more communication between healthcare stakeholders to see where to apply AI.

If you talk to payers, and they dont know who pharma or big data or artificial intelligence, they think Im going to get screwed. So how does this trust gap get crossed? Kindler said during the Light Forum. Historically, pharma and device manufacturers were not distinguishing between the two because the data wasnt available; it was like throwing darts. But as AI and machine learning becomes more robust, you will have a separation between costs of operation and costs that dont matter because they are increasing efficiency.

Efficiency is a key area for drug development, especially in light of shakeups at the FDA that could make AI even more readily impactful.

I work in an industry where it takes 12 years to launch a product, Judy Sewards, Pfizers vice president of digital strategy and data innovation said at the Light Forum. Thats three presidential terms, or three World Cups. Over that time, it takes 1,600 scientists to look at research and 3,600 clinical trials involving thousands of patients. Where we start to think about AI is how can we speed up the process, make it smarter, connect breakthrough medicine and connect patients who need it the most? Whats bringing that to life, Sewards said, is the work they are doing with IBM Watson on immunocology.

Some worry that machines or AI will replace scientists or doctors, but it is actually more like they are the ultimate research assistant, or wingman, she said.

Rajeev Ronanki, Deloittes principal in life sciences and healthcare, told Beckers Hospital Review there needs to be a confluence of three powerful forces to drive the machine learning trend forward: exponential data growth, faster distributed systems, and smarter algorithms that interpret and process that data. When that trifecta comes together, Ronanki forecasts CIOs can expect returns in the form of cognitive insights to augment human decision-making, AI-based engagement tools, and AI automation within devices and processes to develop deep domain-specific expertise.

We expect the growth to continue, with spending on machine intelligence expected to rise to $31.3 billion, Ronanki told Beckers, citing an IDC report.

Where we are today is ground zero, basically, Roam Analytics CEO and cofounder Alex Turkeltaub said during the Light Forum. Were more or less figuring out the commercial pathway, and at best using masters level statistics, no more than that, because its hard to put data together and deal with regulation. Most of even the most cutting-edge deep learning algorithms were developed in the 60s, which were based on ideas from the 1600s. Weve got to figure out a better way.

Especially, since, as Pfizers Judy Sewards pointed out: In our industry, you need to be 100 percent. Error is someones life.

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In-Depth: AI in Healthcare- Where we are now and what's next - MobiHealthNews

Screenshot/HomestarRunner.com

Humans arent nearly as creative as we think. Craft brewers, for example, have run out of fun names and are sending each other cease and desist letters for coming up with the same ideas. So, what if we let computers come up with new names for us?

Thats the problem optics industry research scientist Janelle Shane has been trying to solve using neural networks, but with paint colors. The initial results are downright ridiculous. Like Stanky Bean and Sindis Poop.

By problem, I actually mean she was just trying to have a good time online. What inspired me was I found a post online from someone whod done neural network cookbook recipes, she told Gizmodo. I thought they were hilarious and I wanted more, but there werent any more. The only way to get more was to make more.

Neural networks are essentially computer systemsthat can be trained on large datasets to solve problems like speech or pattern recognition. Shane analyzed a list of 7700 paint colors from Sherwin Williams with a neural network called char-rnn, including both the paint names and their red, blue, and green values.

Once a neural network is trained, it can learn to find the next logical thing based on an input, which is how we ended up with those strange dog pictures last year. In this case, the neural network starts with a letter, then picks the next most logical letter (or a letter further down on the list, depending on the creativity setting) to create pronounceable words. Its like a child learning to speak if its parents only spoke about paint colors.

Shane had the network spit out names during checkpoints as it was learning, at varying levels of creativity. Naturally, its most creative setting eventually started spitting out gibberish:

(I am having trouble breathing from how hard I am laughing right now.)

But eventually, it learned to make some really wacky paint names.

Im sure Hurky wasnt in the original dataset, said Shane. But somehow its come up with that.

Shane previously trained a neural network to come up with new recipe names, creating some of the funniest combinations of words imaginable.

Its tempting to correct the spelling if it almost spells a word, but somehow that takes the fun out of it, said Shane. This is as it comes out of the computer, Im not changing a thing.

Shanes just doing this for fun, but heres the link to char-rnn if youve got your own ideas.

[Postcards from the Frontiers of Science]

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The First AI-Generated Paint Names Include 'Homestar Brown' and 'Stanky Bean' - Gizmodo