Daily Archives: June 30, 2017

Do not go gentle into that good night, Old age should burn and rave at close of day; Rage, rage against the dying of the light.

Welsh poet Dylan Thomas famous lines are a passionate plea to fight against the inevitability of death. While the sentiment is poetic, the reality is far more prosaic. We are all going to die someday at a time and place that will likely remain a mystery to us until the very end.

Or maybe not.

Researchers are now applying artificial intelligence, particularly machine learning and computer vision, to predict when someone may die. The ultimate goal is not to play the role of Grim Reaper, like in the macabre sci-fi Machine of Death universe, but to treat or even prevent chronic diseases and other illnesses.

The latest research into this application of AI to precision medicine used an off-the-shelf machine-learning platform to analyze 48 chest CT scans. The computer was able to predict which patients would die within five years with 69 percent accuracy. Thats about as good as any human doctor.

The results were published in the Nature journal Scientific Reports by a team led by the University of Adelaide.

In an email interview with Singularity Hub, lead author Dr. Luke Oakden-Rayner, a radiologist and PhD student, says that one of the obvious benefits of using AI in precision medicine is to identify health risks earlier and potentially intervene.

Less obvious, he adds, is the promise of speeding up longevity research.

Currently, most research into chronic disease and longevity requires long periods of follow-up to detect any difference between patients with and without treatment, because the diseases progress so slowly, he explains. If we can quantify the changes earlier, not only can we identify disease while we can intervene more effectively, but we might also be able to detect treatment response much sooner.

That could lead to faster and cheaper treatments, he adds. If we could cut a year or two off the time it takes to take a treatment from lab to patient, that could speed up progress in this area substantially.

In January, researchers at Imperial College London published results that suggested AI could predict heart failure and death better than a human doctor. The research, published in the journal Radiology, involved creating virtual 3D hearts of about 250 patients that could simulate cardiac function. AI algorithms then went to work to learn what features would serve as the best predictors. The system relied on MRIs, blood tests, and other data for its analyses.

In the end, the machine was faster and better at assessing risk of pulmonary hypertensionabout 73 percent versus 60 percent.

The researchers say the technology could be applied to predict outcomes of other heart conditions in the future. We would like to develop the technology so it can be used in many heart conditions to complement how doctors interpret the results of medical tests, says study co-author Dr. Tim Dawes in a press release. The goal is to see if better predictions can guide treatment to help people to live longer.

These sorts of applications with AI to precision medicine are only going to get better as the machines continue to learn, just like any medical school student.

Oakden-Rayner says his team is still building its ideal dataset as it moves forward with its research, but have already improved predictive accuracy by 75 to 80 percent by including information such as age and sex.

I think there is an upper limit on how accurate we can be, because there is always going to be an element of randomness, he says, replying to how well AI will be able to pinpoint individual human mortality. But we can be much more precise than we are now, taking more of each individuals risks and strengths into account. A model combining all of those factors will hopefully account for more than 80 percent of the risk of near-term mortality.

Others are even more optimistic about how quickly AI will transform this aspect of the medical field.

Predicting remaining life span for people is actually one of the easiest applications of machine learning, Dr. Ziad Obermeyer tells STAT News. It requires a unique set of data where we have electronic records linked to information about when people died. But once we have that for enough people, you can come up with a very accurate predictor of someones likelihood of being alive one month out, for instance, or one year out.

Obermeyer co-authored a paper last year with Dr. Ezekiel Emanuel in the New England Journal of Medicine called Predicting the FutureBig Data, Machine Learning, and Clinical Medicine.

Experts like Obermeyer and Oakden-Rayner agree that advances will come swiftly, but there is still much work to be done.

For one thing, theres plenty of data out there to mine, but its still a bit of a mess. For example, the images needed to train machines still need to be processed to make them useful. Many groups around the world are now spending millions of dollars on this task, because this appears to be the major bottleneck for successful medical AI, Oakden-Rayner says.

In the interview with STAT News, Obermeyer says data is fragmented across the health system, so linking information and creating comprehensive datasets will take time and money. He also notes that while there is much excitement about the use of AI in precision medicine, theres been little activity in testing the algorithms in a clinical setting.

Its all very well and good to say youve got an algorithm thats good at predicting. Now lets actually port them over to the real world in a safe and responsible and ethical way and see what happens, he says in STAT News.

Preventing a fatal disease is one thing. But preventing fatal accidents with AI?

Thats what US and Indian researchers set out to do when they looked over the disturbing number of deaths occurring from people taking selfies. The team identified 127 people who died while posing for a self-taken photo over a two-year period.

Based on a combination of text, images and location, the machine learned to identify a selfie as potentially dangerous or not. Running more than 3,000 annotated selfies collected on Twitter through the software resulted in 73 percent accuracy.

The combination of image-based and location-based features resulted in the best accuracy, they reported.

Whats next? A sort of selfie early warning system. One of the directions that we are working on is to have the camera give the user information about [whether or not a particular location is] dangerous, with some score attached to it, says Ponnurangam Kumaraguru, a professor at Indraprastha Institute of Information Technology in Delhi, in a story by Digital Trends.

This discussion begs the question: Do we really want to know when were going to die?

According to at least one paper published in Psychology Review earlier this year, the answer is a resounding no. Nearly nine out of 10 people in Germany and Spain who were quizzed about whether they would want to know about their future, including death, said they would prefer to remain ignorant.

Obermeyer sees it differently, at least when it comes to people living with life-threatening illness.

[O]ne thing that those patients really, really want and arent getting from doctors is objective predictions about how long they have to live, he tells Marketplace public radio. Doctors are very reluctant to answer those kinds of questions, partly because, you know, you dont want to be wrong about something so important. But also partly because theres a sense that patients dont want to know. And in fact, that turns out not to be true when you actually ask the patients.

Stock Media provided by photocosma / Pond5

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Artificial Intelligence Predicts Death to Help Us Live Longer - Singularity Hub

Worried that the rise of Artificial Intelligence technologies will make the role of the salesperson obsolete? Maybe you should be... but not if you focus on what really matters where sales, and customers relationships, are concerned. Embrace AI and you might find yourself becoming an even better salesperson.

How?

The following is from Justin Shriber, Vice President of Marketing, LinkedIn Sales Solutions.

Here's Justin:

Over the past year, AI has taken the world by storm. In 2016, AI startups saw record highs in deals and funding, while tech companies like Facebook, Amazon and Google banded together to conduct AI research and promote best practices. A recent Bloomberg Terminal analysis even revealed the number of companies mentioning "artificial intelligence" in their quarterly earnings shot up from under 20 in 2014 to nearly 200 today.

While AI will improve the workplace (think virtual assistants), people worry it will also kill jobs. Manufacturing jobs have already been lost to automation, while self-driving cars and trucks are well on their way to replacing professional drivers. McKinsey reports that machines or robots can take over 49 percent of worker activities, such as stocking supermarket shelves, serving food at restaurants and crunching numbers.

Even sales professionals, whose skill sets are in high demand, are fearful. Forrester predicts that 1 million B2B sales people will lose their jobs to self-service eCommerce by 2020. If that prediction pans out, that's 20 percent of the B2B sales force, gone, three years from now. No wonder everyone is scared.

But let's be clear -- not all sales are the same. While some purely transactional sales positions will move to this self-serve model, jobs that involve selling "high consideration" products through a complex sales process will be enhanced, not replaced, by AI.

Like other professions, sales involves some repetitive tasks that could be easily automated, and while AI will certainly change how we work, it will never replace all salespeople. In fact, it may actually make them better at their jobs. Here's why:

By automating mundane work, AI will save salespeople time.

Automation is already starting to replace rote tasks, which benefits busy sales professionals. Calendly, for example, automatically schedules meetings and sends invites. This frees up salespeople's time for more important tasks that require critical thinking, such as crafting customized emails or teeing up a conversation with a prospective buyers.

But let's take things a step further. One of the biggest challenges for sales professionals is prioritizing their time. Instead of guessing whether now is the best time to reach out, or keeping track of all correspondence with dozens of prospects, sales reps could rely on AI to determine when and how to take their "next best action" to move a deal forward.

Not all data is stored in computers.

One of the most exciting possibilities of AI is its potential to analyze vast amounts of data. In the future, AI will seamlessly digest data and provide smart suggestions, such as prompting you to follow up with a prospective buyer after a phone call. We're already starting to see this kind of technology in its early stages.

For example, Salesforce's Einstein, a smart cloud analytics platform, learns your CRM data, email, calendar, social, ERP and IoT, and delivers predictions and recommendations based on your goals. It can even suggest next steps if it detects a change in customer sentiment.

If selling enterprise software was as simple as processing data from a single source and spitting out the optimal decision, we'd all be toast. Luckily it's not. Data that informs these decisions comes from all kinds of sources, including the human brain.

Great sales professionals can read the room, connect the dots, and make sense of the intangibles that make each deal unique. Statistics do inform purchasing decisions, but reason has its limitations. Other types of data that humans excel at-- like observing others' emotions and body language and reacting immediately -- still factor in.

Relationships still drive business.

Enterprise sales is high stakes by nature. Deals often exceed six figures, and on average, 6.8 people are involved in the buying process. This means several people's careers and reputations are on the line if something goes wrong. I've seen people fired over poor technology decisions or deployments. Unsurprisingly, risk makes people uneasy. That's why a salesperson's job is so crucial.

When it comes down to making a huge purchasing decision, buyers need to trust their sellers. They want to meet that person and ask questions; they want to make sure their fears, hesitations and needs are understood. In an ideal buyer-seller relationship, sales professionals make their buyers feel informed, secure and comfortable with the product. Emotional intelligence is therefore crucial to the process.

Because sales touches on deep emotions like trust and empathy, it's one of the biggest reasons salespeople will never lose their jobs to machines. Robots still haven't grasped natural language understanding, let alone the subtle nuances of emotions. Amazon's Alexa and Apple's Siri, for instance, rely on pre-defined scripts and are easily baffled by simple questions.

While AI's ability to interpret language and emotion will definitely improve, it's unlikely it will ever fully replace the human ability to connect and build trust.

Over the past few years, machines have made great strides in becoming more human-like, from walking on two legs to understanding language. But humans are complex creatures that have evolved over six million years. By comparison, machines are in their infancy. While AI will take over some transactional sales positions, it won't replace sellers who manage intricate, multi-million dollar deals involving executive stakeholders.

Many job functions thatrequire human connection, like sales, just aren't that easy to replace.

Read this article:

3 Reasons Why Artificial Intelligence Will Never Replace Sales Jobs - Inc.com

Google may be an "AI first" company, but few people who work there actually use the term artificial intelligence.

That's because it doesn't actually describe the seismic shift currently happening across all of the Alphabet Inc. (GOOGL) unit's products.The better word for that process is machine learning, which is the technology that's making our computers think and act more like humans, said Peter Norvig, an AI scientist and a director of research at Google.

"Sundar has come out and said we're an AI first company, and that's a pretty bold statement," Norvig told The Street. "Internally we use machine learning more...it's what we're going to use to become an AI-first company."

Google CEO Sundar Pichai has been charting a transformation at the company ever since he took over as chief executive in 2015. Google's next big step is to navigate a future where mobile devices fade away and are replaced by omnipresent intelligence assistants -- an "AI first world," as Pichai has said.

But before that future can become a reality, Silicon Valley giants will have to overcome the obstacle of helping average people understand just what exactly AI is, as well as how it can be used in their everyday lives. The invention of products such as Google Assistant, Amazon.com Inc.'s (AMZN) Alexa and Apple Inc.'s (AAPL) Siri has demystified a lot of the confusion surrounding AI, Norvig said. It's helped people realize that AI isn't going to materialize as Skynet from "The Terminator" oras the so-called singularity-- the theory that one day machines will become smarter than humans.

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One of Google's Top Scientists Explains Artificial Intelligence's Biggest Challenge Right Now - TheStreet.com

Instagram started to automatically block offensive comments Thursday, using artificial intelligence to go beyond simple keyword filters. The use of this technology is also a test case for Facebook as it is looking to improve its own moderation and filtering.

The Facebook-owned photo sharing service officially announced the launch of a new comment filter Thursday morning. Many of you have told us that toxic comments discourage you from enjoying Instagram and expressing yourself freely, wrote Instagram CEO and co-founder Kevin Systrom in a blog post. To help, weve developed a filter that will block certain offensive comments on posts and in live video.

Instagram also announced a new spam filter, which it had quietly been testing over the last couple of months. Filtering abusive comments will for now only be available in English, but spam is being detected if its written inSpanish, Portuguese, Arabic, French, German, Russian, Japanese, or Chinese as well. Comment filters are enabled by default, but can be turned off by each user.

Both filters are powered by machine learning, which means that the technology used to filter comments has been trained with a test set of data, and is looking at not just keywords but also contexts and relationships. An f-word between friends may have a completely different meaning than a slur hurled at an outsider, and song lyrics can include a lot of offensive language without actually offending anyone.

Instagrams comment and spam filters are based on DeepText, an artificial intelligence effort developed in-house at Facebook, as Wired reported Thursday.

Thats notable in part because Facebook itself has yet to officially commit to AI as a means to moderate content and comments. Executives previously said that it may take some time before AI can play a role in moderation, and Facebook has responded to recent controversies related to inappropriate content with the hire of thousands of additional human moderators.

However, more recently, the company seems to have changed its tune on AI a bit. Theres an area of real debate about how much we want AI filtering posts on Facebook, wrote CEO Mark Zuckerberg in a blog post earlier this month. Its a debate we have all the time and wont be decided for years to come. But in the case of terrorism, I think theres a strong argument that AI can help keep our community safe and so we have a responsibility to pursue it.

Does this mean that Facebook may also eventually use AI to moderate comments the way Instagram is now? A Facebook spokesperson stressed that both platforms are unique in a statement emailed toVariety:

Facebook and Instagram are different platforms with different user experiences from the follow model to how comments are used. Although we share the same goal of creating safe communities, we are going to have different approaches. Instagrams new tools are a great first step that both companies will be able to learn from.

In other words: Facebook may not copy Instagrams new AI-powered comment filters 1:1, but the company surely is looking to this as a test case as it evaluates if and how it may one day use artificial intelligence for moderation on Facebook proper as well.

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Instagram Starts Using Artificial Intelligence to Moderate Comments. Is Facebook Up Next? - Variety

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Microsoft is working to bring artificial intelligence to extremely small devices, but the work requires compression techniques and other technologies so it can run on the tiniest of CPUs.

The Raspberry Pi 3 is one of the most popular devices you can buy. For $35, this inexpensive all-in-one computer board can be used to power numerous projectsranging from miniature Linux-based PCsto gaming consolesand more. Now Microsoft reports that it built a device based on the Raspberry Pi 3 that uses artificial intelligence to hunt down pesky rodents.

The device was created by the head of Microsofts Machine Learning and Optimization group, Ofer Dekel. He discovered that squirrels were stealing flower bulbs along with seeds in the bird feeder in his backyard garden. Naturally, he couldnt literally keep watch in the shadows and chase down the furry-tail rodents with his bare hands, so he came up with a plan.

Using his team located at the Redmond, Washington, research lab (he has one in India, too), they trained a computer-vision model to detect squirrels. The artificial intelligence was then deployed onto a Raspberry Pi 3 board inside a special device he mounted in his backyard. Thus, when a squirrel rears its head, the device will turn on the sprinkler system, thwarting the rodents thieving habits.

This backyard project is just part of Microsofts overall picture of an artificial intelligence-first world. Were moving from what is todays mobile-first, cloud-first world to a new world that is going to be made up of an intelligent cloud and intelligent edge, Microsoft CEO Satya Nadella saidduring the recent Build developer conference.

The big accomplishment in the squirrel-hunting project, according to Microsoft, was cramming a deep neural network onto an extremely small chip. Dekel and his team used a variety of techniques to compress the neural network, which is essentially a class of predictors inspired by our brains.

One technique is called weight quantization, capable of cramming more parameters into a smaller physical space. This compression enables the artificial intelligence to work faster, too. Additionally, Dekels group is examining a technique called pruning, which removes redundancies in neural networks. This has a double benefit: the ability to run a neural network on extremely small processors, and faster evaluation times.

However, the team wants to get artificial intelligence running on the smallest ARM-based processor to date: the Cortex M0. According to ARM, this processor has a floorplan area of 0.007mm squared. Thats very, very tiny, and will require the team to make its machine learning models up to 10,000 times smaller than what theyre compressing for the Raspberry Pi 3.

There is just no way to take a deep neural network, have it stay as accurate as it is today, and consume 10,000 less resources. You cant do it, said Dekel. So, for that, we have a longer-term approach, which is to start from scratch. To start from math on the white board and invent a new set of machine-learning technologies and tools that are tailored for these resource-constrained platforms.

To see what the team is currently working on, early previews can be downloaded from Microsofts GitHub depot here. It also provides previews of the compression techniques and training algorithms.

Read more here:

Microsoft manages to cram artificial intelligence on the Raspberry Pi 3 PC board - Digital Trends