When I tell people that I work at an AI company, they often follow up with, "So, what kind of machine learning/deep learning do you do?" This isn't surprising, as most of the market attention (and hype) in and around AI has been centered around machine learning and its high-profile subset deep learning and around natural language processing with the rise of the chatbot and virtual assistants. But while machine learning is a core component of artificial intelligence, AI is, in fact, more than just ML. So, what does it really mean for an application to be "intelligent"? What does it take to create a system that is artificially intelligent?

Depression is a simple-sounding condition with complex origins that aren't fully understood. Now, machine learning may enable scientists to unpick some of its mysteries in order to provide better treatment. For patients to be diagnosed with Major Depressive Disorder, which is thought to be the result of a blend of genetic, environmental, and psychological factors, they have to display several of a long list of symptoms, such as fatigue or lack of concentration. Once diagnosed, they may receive cognitive behavioral therapy or medication to help ease their condition. But not every treatment works for every patient, as symptoms can vary widely.

The 78-video playlist above comes from a course called Neural Networks for Machine Learning, taught by Geoffrey Hinton, a computer science professor at the University of Toronto. The videos were created for a larger course taught on Coursera, which gets re-offered on a fairly regularly basis. Neural Networks for Machine Learning will teach you about "artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc." The courses emphasizes " both the basic algorithms and the practical tricks needed to get them to work well." It's geared for an intermediate level learner – comfortable with calculus and with experience programming (Python).

Driverless pods have started started carrying members of the public around in North Greenwich, London, as part of the GATEway Project. The autonomous vehicles aren't fitted with a steering wheel or a brake pedal, and instead use a collection of five cameras and three lasers to detect and avoid obstacles on a two-mile route near the O2. They can see up to 100m ahead and are capable of performing an emergency stop if necessary, though they have a top speed of just 10mph. The prototype pods being used in Greenwich can carry four passengers at a time, but each of them will have a trained person on board during the three-week trial. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

Meagan Metzger is the founder of Dcode42, an accelerator program for companies with innovative technology products for which there is a current or potential future government need. Dcode42 recently partnered with Amazon Web Services to help speed the adoption of artificial intelligence and machine learning for problem solving in government. GCN spoke with Metzger about the role of AI in government and ways cloud-based AI can help government solve challenges. The interview has been edited for length and clarity. GCN: What government challenges do you see AI solving?

It's a good sign for the robotics industry that more and more robotics companies are starting to make major announcements at specialized events and trade shows, indicating that their robots are ready for tough, real-world applications. This week at ProMat, "the premier showcase of material handling, supply chain, and logistics solutions," Fetch Robotics is showing off two very new, and very large, stuff-transporting robots. This video shows the Freight 500, which can handle 500 kilograms of payload, or generally something about the size of a "case," which I guess is a standard unit in the area of "material handling, supply chain, and logistics solutions." The Freight 1500 weighs just under 470 kg all by itself, but it's only 35.5 centimeters (14 inches) tall, which is the same height as its smaller siblings. It has lidar sensors front and back, a forward-looking RGBD camera, and can run for up to 9 hours while recharging itself to 90 percent in just an hour.

If you're betting on Silicon Valley stars like Google, Tesla, and Uber to free you from your horrorshow commute with autonomous driving technology, don't. That's the key takeaway from a new report that finds Ford--yes, the Detroit-based, 113-year-old giant--is winning the race to build the self-driving car, with General Motors running a close second. Meanwhile, Waymo--aka Google's driverless car effort--sits in sixth place, with Tesla in twelfth. Uber languishes in sixteenth, behind Honda and barely ahead of startup Nutonomy and China's Baidu. That may sound all kinds of wrong to anyone who has seen Uber, Waymo, and Tesla flaunt their tech, and regards Detroit's old guard as ill-prepared for the robotic future.

"Does the world really need another wedding photographer?" That was the thought that ran through Saskia Nelson's mind when, having spontaneously resigned from her office job at a London Olympics legacy project, she was thinking of her next move. An amateur photographer, she decided four years ago, aged 43, that she was going to go professional. But she hadn't really worked out how, and so she used her three-month notice period to consider her options, one of which was to join the army of wedding snappers. "But I thought, 'I'm not married, it's not my bag, I don't really know anything about it,'" says Saskia.

A new global study finds several new categories of human jobs emerging, requiring skills and training that will take many companies by surprise. The threat that automation will eliminate a broad swath of jobs, across the world economy is now well established. As artificial intelligence (AI) systems become ever more sophisticated, another wave of job displacement will almost certainly occur. But here's what we've been overlooking: Many new jobs will also be created -- jobs that look nothing like those that exist today. In Accenture's global study of more than 1,000 large companies already using or testing AI and machine-learning systems, we identified the emergence of entire categories of new, uniquely human jobs.

Things change pretty much on a daily basis in the world of SEO. Since the announcement of Google's AI machine learning algorithm – RankBrain – in 2015, one of the most discussed topics in SEO galleries is: With Google admitting RankBrain being one of the top three ranking factors, these discussions have become even more worthwhile. In past 3-4 months, we also saw a spike in the number of SERPed members asking the same question. And, multiple posts claiming 2017 as the year of AI and Voice Search, we think it is the right time to dive deeper to understand more about it. To get more clarity on this topic, we decided to go straight to the big guns and find out what they think about it. The responses from each expert are compiled below. Fasten your seat belts and get ready for an awesome ride. Albert Mora is the CEO and co-founder of Seolution, an SEO agency for Shopify e-commerce sites. He has been doing SEO from 1997 and has around 20 years of experience. Follow Albert on Twitter here. Since the beginning of the Internet, artificial intelligence has played a relevant role in the operation of search engines. Logically, the algorithms have been evolving, but the fundamental underlying principle remains the same: search engines want to deliver quality search results to the users. For this reason, if you want a long term sustainable SEO results, you must think about the users first, not about the search engines. Alex has more than 15 years of experience in Digital Marketing, and he is working online since 2002.