Alphabet Jigsaw (formerly Google Ideas) officially unveiled this morning their new tool for fighting toxic speech online, appropriately called Perspective. Powered by a deep-learning model trained on more than 17 million manually reviewed reader comments provided by the New York Times, the model assigns a score to a given passage of text, rating it on a scale from 0 to 100%, similar to statements that human reviewers have previously rated as "toxic." What makes this new approach from Google so different than past approaches is that it largely focuses on language rather than ideas: for the most part you can express your thoughts freely and without fear of censorship as long as you express them clinically and clearly, while if you resort to emotional diatribes and name calling, regardless of what you talk about, you will be flagged. What does this tell us about the future of toxic speech online and the notion of machines guiding humans to a more "perfect" humanity? One of the great challenges in filtering out "toxic" speech online is first defining what precisely counts as "toxic" and then determining how to remove such speech without infringing on people's ability to freely express their ideas.

If news, polls and investment figures are any indication, Artificial Intelligence and Machine Learning will soon become an inherent part of everything we do in our daily lives. Backing up the argument are a slew of innovations and breakthroughs that have brought the power and efficiency of AI into various fields including medicine, shopping, finance, news, fighting crime and more. But the explosion of AI has also highlighted the fact that while machines will plug some of the holes human-led efforts leave behind, they will bring disruptive changes and give rise to new problems that can challenge the economical, legal and ethical fabric of our societies. Here are four issues that need Artificial Intelligence companies need to address as the technology evolves and invades even more domains. From driving trucks to writing news and performing accounting tasks, AI algorithms are threatening middle class jobs like never before.

Recidivism prediction instruments (RPI's) provide decision makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. While such instruments are gaining increasing popularity across the country, their use is attracting tremendous controversy. Much of the controversy concerns potential discriminatory bias in the risk assessments that are produced. This paper discusses several fairness criteria that have recently been applied to assess the fairness of recidivism prediction instruments. We demonstrate that the criteria cannot all be simultaneously satisfied when recidivism prevalence differs across groups. We then show how disparate impact can arise when a recidivism prediction instrument fails to satisfy the criterion of error rate balance.

While great strides have been made in using deep learning algorithms to solve supervised learning tasks, the problem of unsupervised learning - leveraging unlabeled examples to learn about the structure of a domain - remains a difficult unsolved challenge. Here, we explore prediction of future frames in a video sequence as an unsupervised learning rule for learning about the structure of the visual world. We describe a predictive neural network ("PredNet") architecture that is inspired by the concept of "predictive coding" from the neuroscience literature. These networks learn to predict future frames in a video sequence, with each layer in the network making local predictions and only forwarding deviations from those predictions to subsequent network layers. We show that these networks are able to robustly learn to predict the movement of synthetic (rendered) objects, and that in doing so, the networks learn internal representations that are useful for decoding latent object parameters (e.g. pose) that support object recognition with fewer training views. We also show that these networks can scale to complex natural image streams (car-mounted camera videos), capturing key aspects of both egocentric movement and the movement of objects in the visual scene, and the representation learned in this setting is useful for estimating the steering angle. Altogether, these results suggest that prediction represents a powerful framework for unsupervised learning, allowing for implicit learning of object and scene structure.

Machine Learning and AI are fast becoming ubiquitous in data driven businesses, that is to say, an awful lot of businesses. Here I choose a few areas where it's possible that big corporations haven't already eaten everybody's lunch. It's not uncharted territory -- if I could think of the next killer application, I'd be trying to do it! So-called after the California Gold Rush where the purveyors of picks and shovels made a killing (whereas the outcome for prospectors was mixed), the picks and shovels of machine intelligence are hardware, data feeds,and (arguably) the algorithms themselves. But these processors were designed for graphics.

The Federal Trade Commission today announced the agenda for its March 9, 2017, FinTech Forum focusing on the consumer implications of two rapidly developing technologies: artificial intelligence and blockchain. The forum, which is the third in an ongoing event series, will take place from 9:00 a.m. to approximately 12:30 p.m. Pacific Time in Berkeley, California. The event will bring together industry representatives, consumer advocates, government officials, and others with expertise regarding these technologies. The half-day forum will feature two panel discussions. The first panel will focus on the potential benefits and risks for consumers with the use of artificial intelligence, which involves the capability of machines to mimic human thinking or actions such as learning and problem solving, in consumer products or services in fields including personalized financial services.

On the surface, a Google subsidiary's blistering accusation last week that Uber has stolen its driverless car technology looks like any of the thousands of patent lawsuits piling up in Silicon Valley court dockets. This one is different, however. And it's different in ways that could spell bad news for Uber. The lawsuit was filed Thursday in San Francisco federal court by Waymo, a subsidiary of Alphabet Inc. devoted to developing self-driving technology. Waymo is responsible for those bug-shaped cars and other vehicles testing the technology around Northern California.

If news, polls and investment figures are any indication, Artificial Intelligence and Machine Learning will soon become an inherent part of everything we do in our daily lives. Backing up the argument are a slew of innovations and breakthroughs that have brought the power and efficiency of AI into various fields including medicine, shopping, finance, news, fighting crime and more. Gary Vaynerchuk was so impressed with TNW Conference 2016 he paused mid-talk to applaud us. But the explosion of AI has also highlighted the fact that while machines will plug some of the holes human-led efforts leave behind, they will bring disruptive changes and give rise to new problems that can challenge the economical, legal and ethical fabric of our societies. Here are four issues that need Artificial Intelligence companies need to address as the technology evolves and invades even more domains.

If news, polls and investment figures are any indication, Artificial Intelligence and Machine Learning will soon become an inherent part of everything we do in our daily lives. Backing up the argument are a slew of innovations and breakthroughs that have brought the power and efficiency of AI into various fields including medicine, shopping, finance, news, fighting crime and more. We're inviting 250 to exhibit at TNW Conference and pitch on stage! But the explosion of AI has also highlighted the fact that while machines will plug some of the holes human-led efforts leave behind, they will bring disruptive changes and give rise to new problems that can challenge the economical, legal and ethical fabric of our societies. Here are four issues that need Artificial Intelligence companies need to address as the technology evolves and invades even more domains.

A Kansas filmmaker's drone video of the Kansas wheat harvest will premiere next month at the New York City Drone Film Festival. The Wichita Eagle reported Monday http://bit.ly/2lrrePw that his video t is among 32 entries accepted to be shown March 17-19 at the drone film festival. A trailer of the movie shows a race against time as harvest gets underway complete with music, combines, gray skies, and thunder. Armknecht has been capturing farm life in Kansas for the past five years. He says the aerial shots give unique views of the farm and allow the scenery of Kansas to shine. A trailer of the movie shows a race against time as harvest gets underway complete with music, combines, gray skies, and thunder.