Monthly Archives: August 2017

Earlier this month, tech moguls Elon Musk and Mark Zuckerberg debated the pros and cons of artificial intelligence from different corners of the internet. While SpaceXs CEO is more of an alarmist, insisting that we should approach AI with caution and that it poses a fundamental existential risk, Facebooks founder leans toward a more optimistic future, dismissing doomsday scenarios in favor of AI helping us build a brighter future.

I now agree with Zuckerbergs sunnier outlookbut I didnt used to.

Beginning my career as an engineer, I was interested in AI, but I was torn about whether advancements would go too far too fast. As a mother with three kids entering their teens, I was also worried that AI would disrupt the future of my childrens education, work, and daily life. But then something happened that forced me into the affirmative.

Imagine for a moment that you are a pathologist and your job is to scroll through 1,000 photos every 30 minutes, looking for one tiny outlier on a single photo. Youre racing the clock to find a microscopic needle in a massive data haystack.

Now, imagine that a womans life depends on it. Mine.

This is the nearly impossible task that pathologists are tasked with every day. Treating the 250,000 women in the US who will be diagnosed with breast cancer this year, each medical worker must analyze an immense amount of cell tissue to identify if their patients cancer has spread. Limited by time and resources, they often get it wrong; a recent study found that pathologists accurately detect tumors only 73.2% of the time.

In 2011 I found a lump in my breast. Both my family doctor and I were confident that it was a Fibroadenoma, a common noncancerous (benign) breast lump, but she recommended I get a mammogram to make sure. While the original lump was indeed a Fibroenoma, the mammogram uncovered two unknown spots. My journey into the unknown started here.

Since AI imaging was not available at the time, I had to rely solely on human analysis. The next four years were a blur of ultrasounds, biopsies, and surgeries. My well-intentioned network of doctors and specialists were not able to diagnose or treat what turned out to be a rare form of cancer, and repeatedly attempted to remove my recurring tumors through surgery.

After four more tumors, five more biopsies, and two more operations, I was heading toward a double mastectomy and terrified at the prospect of the cancer spreading to my lungs or brain.

I knew something needed to change. In 2015, I was introduced to a medical physicist that decided to take a different approach, using big data and a machine-learning algorithm to spot my tumors and treat my cancer with radiation therapy. While I was nervous about leaving my therapy up to this new technology, itcombined with the right medical knowledgewas able to stop the growth of my tumors. Im now two years cancer-free.

I was thankful for the AI that saved my life but then that very same algorithm changed my sons potential career path.

The positive impact of machine learning is often overshadowed by the doom-and-gloom of automation. Fearing for their own jobs and their childrens future, people often choose to focus on the potential negative repercussions of AI rather than the positive changes it can bring to society.

After seeing what this radiation treatment was able to do for me, my son applied to a university program in radiology technology to explore a career path in medical radiation. He met countless radiology technicians throughout my years of treatment and was excited to start his training off in a specialized program. However, during his application process, the program was cancelled: He was told it was because there were no longer enough jobs in the radiology industry to warrant the programs continuation. Many positions have been lost to automationjust like the technology and machine learning that helped me in my battle with cancer.

This was a difficult period for both my son and I: The very thing that had saved my life prevented him from following the path he planned. He had to rethink his education mid-application when it was too late to apply for anything else, and he was worried that his back up plans would fall through.

Hes now pursuing a future in biophysics rather than medical radiation, starting with an undergraduate degree in integrated sciences. In retrospect, we both now realize that the experience forced him to rethink his career and unexpectedly opened up his thinking about what research areas will be providing the most impact on peoples lives in the future.

Although some medical professionals will lose their jobs to AI, the life-saving benefits to patients will be magnificent. Beyond cancer detection and treatment, medical professionals are using machine learning to improve their practice in many ways. For instance, Atomwise applies AI to fuel drug discovery, Deep Genomics uses machine learning to help pharmaceutical companies develop genetic medicines, and Analytics 4 Life leverages AI to better detect coronary artery disease.

While not all transitions from automated roles will be as easy as my sons pivot to a different scientific field, I believe that AI has the potential to shape our future careers in a positive way, even helping us find jobs that make us happier and more productive.

As this technology rapidly develops, the future is clear: AI will be an integral part of our lives and bring massive changes to our society. Its time to stop debating (looking at you, Musk and Zuckerberg) and start accepting AI for what it is: both the good and the bad.

Throughout the years, Ive found myself on both sides of the equation, arguing both for and against the advancement of AI. But its time to stop taking a selective view on AI, choosing to incorporate it into our lives only when convenient. We must create solutions that mitigate AIs negative impact and maximize its positive potential. Key stakeholdersgovernments, corporates, technologists, and moreneed to create policies, join forces, and dedicate themselves to this effort.

And were seeing great progress. AT&T recently began retraining thousands of employees to keep up with technology advances and Google recently dedicated millions of dollars to prepare people for an AI-dominated workforce. Im hopeful that these initiatives will allow us to focus on all the good that AI can do for our world and open our eyes to the potential lives it can save.

One day, yours just might depend on it, too.

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I was worried about artificial intelligenceuntil it saved my life - Quartz

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Have a question? Ask Siri. Want to order a pizza? Amazon Echo is there for you. There are AI technologies to help us park our car, polish our photos, and automate our time.

The technology hasnt been as widespread in K-12 education, but that could soon change. A recent piece of research predicted that AI in education could see an expansion rate of more than 47 percent by 2021.

Artificial Intelligence can be an excellent supplement for the work of the teacher.

Personalized Tutors for Students

Anyone who has spent time around children understands that they learn at different rates. Some students are also auditory or visual learners. Personalized AI tutors within the classroom could offer an alternative method for teaching students the fundamentals.

The technology is here, and being improved on. IBM and Microsoft are working on classroom applications. Other examples of tutoring AI include Thinkster Math, Carnegie Learning or Third Space Learning. Essentially, AI can help with the basic skills so that teachers can focus on the more advanced, creative-based topics at least until the technology is more developed.

Its conceivable at some point that we could have AI programs that serve as companions to students throughout the course of their K-12 education, collecting data on the student and offering custom solutions along the way.

A New Kind of Teachers Assistant

AI might be able to help the teacher with classroom tasks as well. Since artificial intelligence is good at handling repetitive tasks, it could be an asset for grading. Teachers can use any newfound time to better interact with students.

More Customized Lessons

Teachers could conceivably use AI to create more customized lessons for their students. The information for lessons can be compiled in a more personalized way than what appears inside the course textbook.

Helping Students Learn

Traditionally the K-12 system in the United States has been designed to prepare students for manufacturing work, or helping them develop the skills theyll need as they select a career and stay with an employer for many years.

The reality is that the economy and the workplace are changing. AI can help prepare our grade school students for jobs that dont yet exist. Bringing AI into the classroom can be an innovative tool to help teach students the skills they will need for the future.

Matt Brennan is a marketing copywriter, occasional parenting writer, and journalist in the Chicago area. He is also the author of Write Right-Sell Now.

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Photo credit: Getty Images

Matt Brennan is a marketing copywriter, occasional parenting writer, and journalist in the Chicago area. He is also the author of Write Right-Sell Now.

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How Will Artificial Intelligence Change the Classroom? - The Good Men Project (blog)

Today, the question of artificial intelligence (AI) and its role in future warfare is becoming far more salient and dramatic than ever before. Rapid progress in driverless cars in the civilian economy has helped us all see what may become possible in the realm of conflict. All of a sudden, it seems, terminators are no longer the stuff of exotic and entertaining science-fiction movies, but a real possibility in the minds of some. Innovator Elon Musk warns that we need to start thinking about how to regulate AI before it destroys most human jobs and raises the risk of war.

It is good that we start to think this way. Policy schools need to start making AI a central part of their curriculums; ethicists and others need to debate the pros and cons of various hypothetical inventions before the hypothetical becomes real; military establishments need to develop innovation strategies that wrestle with the subject. However, we do not believe that AI can or should be stopped dead in its tracks now; for the next stage of progress, at least, the United States must rededicate itself to being the first in this field.

First, a bit of perspective. AI is of course not entirely new. Remotely piloted vehicles may not really qualifyafter all, they are humanly, if remotely, piloted. But cruise missiles already fly to an aimpoint and detonate their warheads automatically. So would nuclear warheads on ballistic missiles, if God forbid nuclear-tipped ICBMs or SLBMs were ever launched in combat. Semi-autonomous systems are already in use on the battlefield, like the U.S. Navy Phalanx Close-In Weapons System, which is capable of autonomously performing its own search, detect, evaluation, track, engage, and kill assessment functions, according to the official Defense Department description, along with various other fire-and-forget missile systems.

But what is coming are technologies that can learn on the jobnot simply follow prepared plans or detailed algorithms for detecting targets, but develop their own information and their own guidelines for action based on conditions they encounter that were not initially foreseeable in specific.

A case in point is what our colleague at Brookings, retired Gen. John Allen, calls hyperwar. He develops the idea in a new article in the journal Proceedings, coauthored with Amir Husain. They imagine swarms of self-propelled munitions that, in attacking a given target, deduce patterns of behavior of the targets defenses and find ways to circumvent them, aware all along of the capabilities and coordinates of their teammates in the attack (the other self-propelled munitions). This is indeed about the place where the word robotics seems no longer to do justice to what is happening, since that term implies a largely prescripted process or series of actions. What happens in hyperwar is not only fundamentally adaptive, but also so fast that it far supercedes what could be accomplished by any weapons system with humans in the loop. Other authors, such as former Brookings scholar Peter Singer, have written about related technologies, in a partly fictional sense. Now, Allen and Husain are not just seeing into the future, but laying out a near-term agenda for defense innovation.

The United States needs to move expeditiously down this path. People have reasons to fear fully autonomous weaponry, but if a Terminator-like entity is what they are thinking of, their worries are premature. That software technology is still decades away, at the earliest, along with the required hardware. However, what will be available sooner is technology that will be able to decide what or who is a targetbased on the specific rules laid out by the programmer of the software, which could be highly conservative and restrictiveand fire upon that target without any human input.

To see why outright bans on AI activities would not make sense, consider a simple analogy. Despite many states having signed the Non-Proliferation Treaty, a ban on the use and further development of nuclear weapons, the treaty has not prevented North Korea from building a nuclear arsenal. But at least we have our own nuclear arsenal with which we can attempt to deter other such countries, a tactic that has been generally successful to date. A preemptive ban on AI development would not be in the United States best interest because non-state actors and noncompliant states could still develop it, leaving the United States and its allies behind. The ban would not be verifiable and it could therefore amount to unilateral disarmament. If Western countries decided to ban fully autonomous weaponry and a North Korea fielded it in battle, it would create a highly fraught and dangerous situation.

To be sure, we need the debate about AIs longer-term future, and we need it now. But we also need the next generation of autonomous systemsand America has a strong interest in getting them first.

Michael O'Hanlon is a senior fellow at the Brookings Institution.Robert Karlen is a student at the University of Washington and an intern in the Center for Twenty-First Century Security and Intelligence at the Brookings Institution.

Image: Reuters

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America Can't Afford to Lose the Artificial Intelligence War | The ... - The National Interest Online

I was curious.

The idea of driverless cars has been, and still is, fascinating to me. And so is the technology that makes driverless cars possible -- artificial intelligence (AI). Like many, I grew up enjoying books, movies, and TV shows that featured machines that could think in similar ways as humans. But what was once science fiction has now become reality.

A couple of years ago, I began a quest to really understand how AI works. I already knew the general concepts, but I wanted to delve into the nitty-gritty of one of the most revolutionary technologies of all time. So I read everything I could get my hands on, from fairly high-level books to textbooks to websites geared toward AI developers.

Along the way, I decided to build my own AI system. One thing even more interesting to me than AI is investing, so I decided to combine the two and develop an AI system that could make investment recommendations. That system is now up and running. And it told me just how smart Warren Buffett really is.

Image source: Getty Images.

AI includes quite a few approaches. One that especially intrigued me was artificial neural networks. The idea for artificial neural networks originated back in the 1940s, when aneurophysiologist and a mathematicianteamed up to write a paper about how neurons in the human brain might work. Based on their research, they built a simple neural network using electrical circuits.

Fast-forward to today. Artificial neural networks are used in many AI applications. Facebook (NASDAQ:FB), for example, uses neural networks in recognizing the faces of people in photos and to decide which advertisements to display to which users.Apple (NASDAQ:AAPL) uses neural networks to enable Siri to recognize what people ask and respond to questions.

Artificial neural networks work in a similar way that your neurons do. Each neuron is connected to multiple other neurons. When there is input (for example, a bee sting), the neuron transmits a signal to the neurons to which it's linked. In artificial neural networks, though, the inputs are data -- like images and speech. The artificial neural network learns from when it gets things wrong, self-adjusts, and gets better at recognition the more data it handles.

Image source: Getty Images.

My artificial neural network was child's play compared to what Apple and Facebook use. I created a relatively simple network that received financial input. This input included price, earnings, and valuation history for the S&P 500 index. I also threw consumer price index (CPI) data, prime lending rates, three-month Treasury bill rates, industrial productivity index data, unemployment rates, and other financial data into the mix.

The kind of artificial neural network I built used what's called "supervised learning," where the AI system is trained using a lot of data for which the desired outputs are known. I trained my system using over 50 years of data, going back to the 1940s. I then tested it using data from 2000 through today.

What I wanted the artificial neural network to determine was whether a person should be invested in the S&P 500 or in cash on a month-by-month basis. After a few stumbles along the way, I finally received an answer from the AI system. That answer was: Always be invested in stocks.

I ran that system all kinds of ways. I pared down the inputs. I changed out some data for other data. I experimented with several variables that the AI experts recommend tweaking. And the answer always came back the same.

It occurred to me that my AI system was basically saying to do what legendary investor Warren Buffett wrote in his letter to Berkshire Hathaway (NYSE:BRK-A) (NYSE:BRK-B) shareholders in 2014. He related the instructions in his will for the trustee of his estate to follow upon his death: Invest most of the money in an S&P 500 index fund and let it ride.

Here's the really interesting part. I examined in more detail the recommendations from the artificial neural network. The system had more confidence in being in stocks when the market was going down and less confidence when the market was going up. That's basically what Buffett had in mind when he said to "be fearful when others are greedy and greedy when others are fearful." I realized that I had created a "Buffett-bot"!

Image source: Getty Images.

The more you think about it, though, the more my AI system -- and Warren Buffett -- makes sense. Historically, the stock market has risen a lot more months than it's fallen. Every time the market has fallen, it's come roaring back. There's every reason in the world to be confident when the market is down, because better days will surely be ahead. That's been Buffett's philosophy his entire career.

Of course, Buffett hasn't followed the advice that he is leaving for his heirs. Instead of parking his money in an S&P 500 index fund, he has used Berkshire Hathaway as a vehicle to build his own investment fund of sorts. If you pick the right stocks, your success will be even greater than going only with the S&P 500. Buffett's track record proves the point.

I haven't asked my AI system yet, by the way, which stocks it would recommend. My hunch is that, if it's as smart as I think it is, it would come up with suggestions pretty close to the stock picks made by The Motley Fool's investing newsletters, which have trounced the S&P 500's performance. (For what it's worth, The Motley Fool long ago recommended several stocks of AI leaders, including both Apple and Facebook, as well as Buffett's Berkshire Hathaway.)

What's the key takeaway from my experiment with AI? Invest in stocks, stay invested in stocks, and buy even more when others are too afraid to do so. The concept applies to the S&P 500 or solid individual stocks like The Motley Fool recommends. That's an intelligent approach -- whether the intelligence is artificial or not.

Keith Speights owns shares of Apple and Facebook. The Motley Fool owns shares of and recommends Apple, Berkshire Hathaway (B shares), and Facebook. The Motley Fool has a disclosure policy.

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I Built an Artificial-Intelligence System for Investing -- and It Showed How Smart Warren Buffett Is - Motley Fool

A

utomation could replace one-third of U.S. jobs within 15 years. Oxford and Yale experts recently predicted that artificial intelligence could outperform humans in a variety of tasks by 2045, ranging from writing novels to performing surgery and driving vehicles. A little human rage would be a natural response to such unsettling news.

Artificial intelligence (AI) is bringing us to the precipice of an enormous societal shift. We are collectively worrying about what it will mean for people. As a doctor, Im naturally drawn to thinking about AIs impact on the practice of medicine. Ive decided to welcome the coming revolution, believing that it offers a wonderful opportunity for increases in productivity that will transform health care to benefit everyone.

Groundbreaking AI models have bested humans in complex reasoning games, like the recent victory of Googles AlphaGo AI over the human Go champ. What does that mean for medicine?

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To date, most AI solutions have solved minor human issues playing a game or helping order a box of detergent. The innovations need to matter more. The true breakthroughs and potential of AI lie in real advancements in human productivity. A McKinsey Global Institute report suggests that AI is helping us approach an unparalleled expansion in productivity that will yield five times the increase introduced by the steam engine and about 1 1/2 times the improvements weve seen from robotics and computers combined. We simply dont have a mental model to comprehend the potential of AI.

Across all industries, an estimated 60 percent of jobs will have 30 percent of their activities automated; about 5 percent of jobs will be 100 percent automated.

What this means for health care is murky right now. Does that 5 percent include doctors? After all, medicine is a series of data points of a knowable nature with clear treatment pathways that could be automated. That premise, though, fantastically overstates and misjudges the capabilities of AI and dangerously oversimplifies the complexity underpinning what physicians do. Realistically, AI will perform many discrete tasks better than humans can which, in turn, will free physicians to focus on accomplishing higher-order tasks.

If you break down the patient-physician interaction, its complexity is immediately obvious. Requirements include empathy, information management, application of expertise in a given context, negotiation with multiple stakeholders, and unpredictable physical response (think of surgery), often with a life on the line. These are not AI-applicable functions.

I mentioned AlphaGo AI beating human experts at the game. The reason this feat was so impressive is due to the high branching factor and complexity of the Go game tree there are an estimated 250 choices per move, permitting estimates of 10 to the 170th different game outcomes. By comparison, chess has a branching factor of 35, with 10 to the 47th different possible game outcomes. Medicine, with its infinite number of moves and outcomes, is decades away from medical approaches safely managed by machines alone.

We still need the human factor.

That said, more than 20 percent of a physicians time is now spent entering data. Since doctors are increasingly overburdened with clerical tasks like electronic health record entry, prior authorizations, and claims management, they have less time to practice medicine, do research, master new technology, and improve their skills. We need a radical enhancement in productivity just to sustain our current health standards, much less move forward. Thoughtfully combining human expertise and automated functionality creates an augmented physician model that scales and advances the expertise of the doctor.

Physicians would rather practice at the top of their licensing and address complex patient interaction than waste time entering data, faxing (yes, faxing!) service authorizations, or tapping away behind a computer. The clerical burdens pushed by fickle health care systems onto physicians and other care providers is both unsustainable and a waste of our best and brightest minds. Its the equivalent of asking an airline pilot to manage the ticket counter, count the passengers, handle the standby and upgrade lists, and give the safety demonstrations then fly the plane. AI can help with such support functions.

But to radically advance health care productivity, physicians must work alongside innovators to atomize the tasks of their work. Understanding where they can let go to unlock time is essential, as is collaborating with technologists to guide truly useful development.

Perhaps it makes sense to start with automated interpretation of basic labs, dose adjustment for given medications, speech-to-text tools that simplify transcription or document face-to-face interactions, or even automate wound closure. And then move on from there.

It will be important for physicians and patients to engage and help define the evolution of automation in medicine in order to protect patient care. And physicians must be open to how new roles for them can be created by rapidly advancing technology.

If it all sounds a bit dreamy, I offer an instructive footnote about experimentation with AlphaGo AI. The recent game summit proving AlphaGos prowess also demonstrated that human talent increases significantly when paired with AI. This hybrid model of humans and machines working together presents a scalable automation paradigm for medicine, one that creates new tasks and roles for essential medical and technology professionals, increasing the capabilities of the entire field as we move forward.

Physicians should embrace this opportunity rather than fear it. Its time to rage with the machine.

Jack Stockert, M.D., is a managing director and leader of strategy and business development at Health2047, a Silicon Valley-based innovation company.

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Artificial intelligence is coming to medicine don't be afraid - STAT

Government is always being asked to do more with less less money, less staff, just all around less and that makes the idea of artificial intelligence (AI)a pretty attractive row to hoe. If a piece of technology could reduce staff workload or walk citizens through a routine process or form, you could effectively multiply a workforce without ever actually adding new people.

But for every good idea, there are caveats, limitations, pitfalls and the desire to push the envelope. While innovating anything in tech is generally a good thing, when it comes to AI in government, there is fine line to walk between improving a process and potentially making it more convoluted.

Outside of a few key government functions, a new white paper from the Harvard Ash Center for Democratic Governance and Innovation finds that AI could actually increase the burden of government and muddy-up the functions it is so desperately trying to improve.

Hila Mehr, a Center for Technology and Democracy fellow, explained that there are five key government problems that AI might be able to assist with reasonably: resource allocation, large data sets, expert shortages, predictable scenarios, and procedural and diverse data.

And governments have already started moving into these areas. In Arkansas and North Carolina, chatbots are helping the state connect with its citizens through Facebook. In Utah and Mississippi, Amazon Alexa skills have been introduced to better connect constituents to the information and services they need.

Unlike Hollywood representations of AI in film, Mehr said, the real applications for artificial intelligence in a government organization are generally far from sexy. The administrative aspects of governing are where tools like this will excel.

Where it comes to things like expert shortages, she said she sees AI as a means to support existing staff. In a situation where doctors are struggling to meet the needs of all of their patients, AI could act as a research tool. The same is true of lawyers dealing with thousands of pages of case law. AI could be used as a research assistant.

If youre talking about government offices that are limited in staff and experts," Mehr said, "thats where AI trained on niche issues could come in.

But, she warned, AI is not without its problems, namely making sure that it is not furthering human bias written in during the programming process and played out through the data it is fed. Rather than rely on AI to make critical decisions, she argues that any algorithms and decisions made for or as a result of AI should retain a human component.

We cant rely on them to make decisions, so we need that check, the way we have checks in our democracy, we need to have checks on these systems as well, and thats where the human group or panel of individuals comes in, Mehr said. The way that these systems are trained, you cant always know why they are making the decision they are making, which is why its important to not let that be the final decision because it can be a black box depending on how it is trained and you want to make sure that it is not running on its own.

But past the fear that the technology might disproportionately impact certain citizens or might somehow complicate the larger process, there is the somewhat legitimate fear that the implementation of AI will mean lost jobs. Mehr said its a thought that even she has had.

On the employee side, I think a lot of people view this, rightly so, as something that could replace them," she added. "I worry about that in my own career, but I know that it is even worse for people who might have administrative roles. But I think early studies have shown that youre using AI to help people in their work so that they are spending less time doing repetitive tasks and more time doing the actual work that requires a human touch.

In both her white paper and on the phone, Mehr is careful to advise against going whole hog into AI with the expectation that it can replace costly personnel. Instead she advocates for the technology as a tool to build and supplement the team that already exists.

As for where the technology could run affront of human jobs, Mehr advises that government organizations and businesses alike start considering labor practices in advance.

Inevitably, it will replace some jobs, she said. People need to be looking at fair labor practices now, so that they can anticipate these changes to the market and be prepared for them.

With any blossoming technology, there are barriers to entry and hurdles that must be overcome before a useful tool is in the hands of those best fit to use it. And as with anything, money and resources present a significant challenge but Mehr said large amounts of data are also needed to get AI, especially learning systems, off the ground successfully.

If you are talking about simple automation or [answering] a basic set of questions, it shouldnt take that long. If you are talking about really training an AI system with machine learning, you need a big data set, a very big data set, and you need to train it, not just feed the system data and then its ready to go, she said. The biggest barriers are time and resources, both in the sense of data and trained individuals to do that work.

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Study: Government Should Think Carefully About Those Big Plans for Artificial Intelligence - Government Technology