We call it "AI-assisted grading," where machines assist humans in grading consistently and quickly. For many questions, Gradescope's AI will be able to learn how to grade all student submissions from a small number of answers graded by the instructor, such that an instructor would only have to grade about ten answers out of a hundred submissions. For known questions, Gradescope's AI will be able to grade instantly, without any human input. This seems to be the evolving narrative of man and machine, collaborating together for the greater good, software and robots becoming "partners" in work rather than usurpers of it. And for the time stressed teacher or teaching assistant, having to "grade" ten essays instead of hundreds is certainly appealing.

Today the Zillow Group is a public company with 645 million in revenue that also operates websites for mortgage and real estate professionals -- and completed the acquisition of its nearest competitor, Trulia, last year. From the start, Zillow offered the "Zestimate," its value-forecasting feature for homes in locations across the United States. Currently, Zillow claims to have Zestimates for more than 100 million homes, with 100-plus attributes tracked for each property. The technology powering Zestimates and other features has advanced steadily over the years, with open source and cloud computing playing increasingly important roles. Last week I interviewed Stan Humphries, chief analytics officer at Zillow, along with Jasjeet Thind, senior director of data science and engineering.

How does the image-recognition technology in a self-driving car respond to a blurred shape suddenly appearing on the road? Researchers from KU Leuven, Belgium, have shown that machines can learn to respond to unfamiliar objects like human beings would. Imagine heading home in your self-driving car. The rain is falling in torrents and visibility is poor. All of a sudden, a blurred shape appears on the road.

Ordinarily I greet Facebook events with a big, indifferent yawn. But the Facebook F8 developer show earned my eyeballs (full video replays here). The big question is: why bother? F8's centerpiece announcements pertained to Facebook Messenger – not exactly the app enterprise IT managers are eager to incorporate. But Facebook Messenger is no longer just a place to stalk annoy your old schoolmates. With the debut of several new bot-powered Messenger services, the commercial viability of AI (artificial intelligence) services is a pressing debate.

When you think of artificial intelligence, the first image that likely comes to mind is one of sentient robots that walk, talk and emote like humans. It's known as machine learning, and it revolves around enlisting computers in the task of sorting through the massive amounts of data that modern technology has allowed us to generate (a.k.a. One of the places machine learning is turning out to be the most beneficial is in the environmental sciences, which have generated huge amounts of information from monitoring Earth's various systems -- underground aquifers, the warming climate or animal migration, for example. A slew of projects have been popping up in this relatively new field, called computational sustainability, that combine data gathered about the environment with a computer's ability to discover trends and make predictions about the future of our planet. This is useful to scientists and policy-makers because it can help them develop plans for how to live and survive in our changing world.

Royal Bank of Scotland (RBS), an organization whose brands provide financial products and services to personal and business customers in the UK and beyond, is putting artificial intelligence (AI) to work in its customer support department. Luvo, a web chat application, is being used to help EBS customer support staff when they aren't sure of an answer to a customer's question. Luvo is capable of fielding questions by sifting through a vast amount of data very quickly. If an answer can't be determined, the application directs the question to a human who can answer more challenging questions. Think of Luvo as live chat, but instead of a human responding, it's a machine. The AI application was originally tested in a two-month trial of 1,200 employees who managed relationships with small businesses, before it was deemed successful.

Congratulations are in order for the folks at Google Deepmind who have mastered Go. However, some of the discussion around this seems like giddy overstatement. Wired says Machines have conquered the last games and Slashdot says We know now that we don't need any big new breakthroughs to get to true AI. The truth is nowhere close. For Go itself, it's been well-known for a decade that Monte Carlo tree search (i.e.

I have trained a SVM and logistic regression classifier on my dataset for binary classification. Both classifier provide a weight vector which is of the size of the number of features. I can use this weight vector to select the 10 most important features. For doing that I have turned the weights into t-scores by doing a permutation test. I did 1000 permutations of the class labels and at each permutation I calculated the weight vector.

After introducing the scikit-learn API, we'll use it to explore the basic categories of machine-learning problems and related topics such as feature selection and model validation and practice applying these tools to real-world datasets.

After introducing the scikit-learn API, we'll use it to explore the basic categories of machine-learning problems and related topics such as feature selection and model validation and practice applying these tools to real-world datasets.