… Software developers will be able to build better software faster, using AI technologies such as advanced machine learning (ML), deep learning, natural language processing, and business rules, says Forrester analyst Diego Lo Giudice.
Machine learning and deep learning are the core AI technologies developers need to master to build applications that learn on their own, Lo Giudice says. “Developers will focus less on using coded rules to program applications to be smart and instead program algorithms or configure them to self-learn,” he writes in the October 2016 Forrester report How AI Will Change Software Development and Applications. “Devs will integrate algorithms, compose, and source large data sets to train and test such apps.”
Todd Schiller, head of engineering at MOKA, a disruptive technologies advisory firm, believes AI holds great opportunity for developers. “In many ways, it will be similar to the impact of open source and social software development [such as] GitHub, Stack Overflow. By tapping into the collective intelligence of the community, software engineers have gained an immense amount of leverage.”
Since the birth of photography almost 180 years ago, the relationship between a photographer and a camera has remained mostly unchanged. You open a shutter and capture an image. Though you might manipulate lenses, exposures, and chemicals—or, in recent years, bits—there was a nearly one-to-one relationship between what the lens saw and what you captured. But you’ve likely taken thousands, if not tens of thousands, of pictures in recent years that break that relationship without knowing it.
Computational photography takes a swarm of data from images or image sensors and combines it algorithmically to produce a photo that would be impossible to capture with film photography or digital photography in its more conventional form. Image data can be assembled across time and space, producing super-real high-dynamic range (HDR) photos—or just ones that capture both light and dark areas well. Multiple cameras’ inputs can be fused into a single image, as on some Android phones and the iPhone 7 Plus, allowing for crisper or richer images in a single shot and a synthetic zoom that looks nearly as good as one produced via optical means.
Life was long thought to obey its own set of rules. But as simple systems show signs of lifelike behavior, scientists are arguing about whether this apparent complexity is all a consequence of thermodynamics.
Apple has joined the Partnership on AI as a founding member. The company has been involved and collaborating with the Partnership since before it was first announced and is thrilled to formalize its membership alongside Amazon, Facebook, Google/DeepMind, IBM, and Microsoft.
Diversity of thought across the organization is crucial to ensure that we effectively explore and address the influences of AI on people and society, provide guidance on AI best practices, and seek to advance the public’s understanding of AI. We are committed to having balanced representation at the leadership, executive, and operations levels.
Today, we are announcing the inaugural Board of Trustees, the board that oversees the Partnership on AI. First, we’re excited to welcome six new Independent board members. These new members are Dario Amodei (OpenAI), Subbarao Kambhampati (Association for the Advancement of Artificial Intelligence & ASU), Deirdre Mulligan (UC Berkeley), Carol Rose (American Civil Liberties Union), Eric Sears (MacArthur Foundation), and Jason Furman (Peterson Institute of International Economics).
Developing robots that can serve as better nurses, teachers, or sales representatives means developing robots that can support humans. One scenario in which humans are in obvious need of emotional support is when they’re trying to date each other.
In order to investigate whether responsiveness—an important quality in creating emotional bonds between humans—is also important for humans relating to robots, a group of researchers from Northwestern University, Cornell University, the University of Rochester, and the Interdisciplinary Center in Herzliya, Israel, decided to simulate the “stressful interaction” of online dating. The group, whose work was published in the journal Computers in Human Behavior in May, wanted to see if responsive robots could help humans see robot support “as a safe haven in times of need and as a secure base for becoming more confident in a subsequent stressful interaction.”
How will the legal system treat reinforcement learning? What if the AI-controlled traffic signal learns that it’s most efficient to change the light one second earlier than previously done, but that causes more drivers to run the light and causes more accidents?
Traditionally, the legal system’s interactions with software like robotics only finds liability where the developer was negligent or could foresee harm. For example, Jones v. W + M Automation, Inc., a case from New York state in 2007, did not find the defendant liable where a robotic gantry loading system injured a worker, because the court found that the manufacturer had complied with regulations.
But in reinforcement learning, there’s no fault by humans and no foreseeability of such an injury, so traditional tort law would say that the developer is not liable.
Ohio’s Transportation Research Center (TRC) is getting a new 540-acre facility specifically for testing and researching smart mobility solutions, with on-site plans for a 12-lane intersection and reconfigurable test platform wider than 50 highway lines and as long as 10 football fields. That comes courtesy of a new $45 million grant from Ohio State University, Ohio state funds and Jobs Ohio.
For people who play the video game Counter Strike online, it’s hard enough watching your back at the best of times. In the fast-paced first-person shooter, there are always players with quicker reflexes or a sharper eye.
But at the height of its popularity a few years ago, people started to come up against other players with skills that were too good to be true. Games like Counter Strike and Half Life – another shooter that was very popular online – had a problem with players who used software cheats that steadied their aim or let them see through walls.
So in 2006, when the stakes were raised by an online competition with cash prizes, an unusual pair of referees were called in. David Excell and Bill Fitzgerald were mathematicians who had just spun out an artificial intelligence company called Featurespace from their lab at the University of Cambridge. Their software was very good at one thing: spotting weird behaviour.
IEEE Spectrum, Amy Nordrum, Kristen Clark and IEEE Spectrum Staff
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Today’s mobile users want faster data speeds and more reliable service. The next generation of wireless networks—5G—promises to deliver that, and much more. With 5G, users should be able to download a high-definition film in under a second (a task that could take 10 minutes on 4G LTE). And wireless engineers say these networks will boost the development of other new technologies, too, such as autonomous vehicles, virtual reality, and the Internet of Things.
If all goes well, telecommunications companies hope to debut the first commercial 5G networks in the early 2020s. Right now, though, 5G is still in the planning stages, and companies and industry groups are working together to figure out exactly what it will be. But they all agree on one matter: As the number of mobile users and their demand for data rises, 5G must handle far more traffic at much higher speeds than the base stations that make up today’s cellular networks.
Scientists from Tufts University’s School of Arts and Sciences, the Allen Discovery Center at Tufts, and the University of Maryland, Baltimore County have used artificial intelligence to gain insight into the biophysics of cancer. Their machine-learning platform predicted a trio of reagents that was able to generate a never-before-seen cancer-like phenotype in tadpoles. The research, reported in Scientific Reports on January 27, shows how artificial intelligence (AI) can help human researchers in fields such as oncology and regenerative medicine control complex biological systems to reach new and previously unachievable outcomes.
University Data Science News
UC-Berkeley Assistant Professor Norman Yao is writing papers on “time crystals” that are structured across time, working in four dimensions the way crystals are usually thought to exist three. How wondrous, “they are the first of a large class of new materials that are intrinsically out of equilibrium, unable to settle down to the motionless equilibrium of, for example, a diamond or ruby.”
University of Cambridge mathematicians David Excell and the late Bill Fitzgerald are using AI to detect cheating. They started by looking at data from video games Counter Strike and Half Life, and adapted the technology to find cheating in organizations like banks.
Howard Hughes Medical Institute has released new guidelines strongly encouraging its researchers at Janelia and grantees to publish in Open Access journals or otherwise make sure their work is available to the public, for free, as soon as possible and no later than 12 months after publication. Readers, do you know if HHMI pays the Open Access publishing fees associated with some of the top journals?
Allison Tillack, a medical anthropologist, wrote her dissertation on how the profession of radiology changed after new imaging technology spread through the field. KQED has a condensed version of her work that is this week’s long read. Radiologists went from being among the most satisfied, happiest of doctors in 2005 to suffering from loneliness. After being physically displaced by a new machine, a doctor ran a tiny personal experiment: “I tried to go the whole day without speaking to anyone. And that’s what happened.”
UC-Berkeley research engineer Steven Shladover suggests that biking in traffic could get even more dangerous. AI for self-driving cars struggles to handle the fast or slow, swervy or straight-pathed, bulky or sleek diversity of bikers and biking behavior. Meanwhile in Boston, nuTonomy, co-founded by MIT researcher Karl Iagnemma, is testing self-driving cars on the road already, with a human back-up driver just in case.
In a first for across-the-pond collaboration between data science institutes, the Michigan Institute of Data Science at the University of Michigan and the Centre for Data Science and Big Data Institute at University College London signed a five-year agreement of scientific and academic cooperation.
David Venturi dropped out of his Master’s degree program in Computer Science at the University of Toronto. And opted in to a masters in Data Science of his own creation. One exercise in his DIY program, ranking the best publicly available data science intro courses.
In April 2010, a couple of months after Heather’s email, the hidden cameras revealed the culprit: a postdoctoral fellow named Vipul Bhrigu, who, confronted with the video, confessed that he had sprayed alcohol into a cell culture medium in the refrigerator. We were in shock. Dr. Bhrigu was the most cooperative, passionate and friendly member of my lab. He’d been at the bottom of our suspect list.
After being taken to the police station, he and the detective returned to my office. “I am sorry,” he told me. “I have disgraced myself, hurt you, hurt the lab and know that you will never forgive me. I felt terrible every time I did this and almost hoped there was a camera. I thought Heather was so smart and I did it to slow her down. It was because of my internal pressure.”
Were we dealing with a sociopath, or was he being honest?
Tiny satellites, some smaller than a shoe box, are currently orbiting around 200 miles above Earth, collecting data about our planet and the universe. It’s not just their small stature but also their accompanying smaller cost that sets them apart from the bigger commercial satellites that beam phone calls and GPS signals around the world, for instance. These SmallSats are poised to change the way we do science from space. Their cheaper price tag means we can launch more of them, allowing for constellations of simultaneous measurements from different viewing locations multiple times a day – a bounty of data which would be cost-prohibitive with traditional, larger platforms.
Called SmallSats, these devices can range from the size of large kitchen refrigerators down to the size of golf balls. Nanosatellites are on that smaller end of the spectrum, weighing between one and 10 kilograms and averaging the size of a loaf of bread.
New York, NY March 27-31. Supervised Neural Time Series is a week-long coding sprint to gather established data scientists, who specialize in high-dimensional neural time series. Together, we will work on advancing popular or upcoming FOSS projects that enable the analysis of a broad class of neural recordings: extracellular neurophysiology (spike trains), electro-corticography (ECoG) and magneto-/electro-encephalography (MEG/EEG).
The Stanford Artificial Intelligence Laboratory’s Outreach Summer program intends to increase diversity in the field of Artificial Intelligence by targeting students from a range of financial and cultural backgrounds. Deadline for applications is April 15.
When you text a friend saying ‘I’ll fall you later’, how does your iPhone know to correct ‘fall’ to ‘call’? Auto-correct owes its prowess to a field that continues to gain paramount importance among computer scientists, and is an especially lively area of study at our very own Center for Data Science: Natural Language Processing (NLP).
Generally speaking, part of NLP research involves calculating ‘the joint probability distribution of words’ in a language. In other words: researchers working in English, for example, use algorithms to analyze large cachets of English documents and texts, and calculate which words most frequently appear beside each other in various contexts, or words that share semantic similarity (synonyms). After identifying dominant word patterns in the English language, researchers can then write programs that will predict what word may come next in a sentence or a paragraph (‘probability distribution’).
With the new year upon us, we felt it was time to give Wrangler an upgrade and incorporate features from the v4 release of Wrangler Enterprise into the free edition.
To encourage everyone to get their hands on the latest release, we’re hosting a contest open to all Wrangler users. We’ll be handing out free t-shirts to those who participate and a select few will be taking home cash prizes.
“Having so many packages available can be a double-edged sword though: it can take some searching to find the package you need. Luckily, there are some resources available to help you.”