Google's work in artificial intelligence is impressive. It includes networks of hardware and software that are very similar to the system of neurons in the human brain. By analyzing huge amounts of data, the neural nets can learn all sorts of tasks, and, in some cases like with AlphaGo, they can learn a task so well that they beat humans. They can also do it better and in a bigger scale. AI seems to be the future of Google Search and of the technology world in general.

Artificial intelligence (AI) is the buzzword in almost all industries. Decision-makers want to make use of massive data they get from various sources. This is where data analytics and artificial intelligence come into play. According to Reportbuyer, the AI market is estimated to grow to 5.05 billion by 2020 at a CAGR of 53.65 per cent from 2015 to 2020. This growth can be attributed to factors such as diversified application areas, improved productivity and increased customer satisfaction. The AI market in APAC is expected to grow at the highest CAGR between 2015 and 2020.

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Symantec vice president global portfolio and solutions product management for enterprise security Bhagwat Swaroop (pictured) has been involved in the process of setting a course for the company following the divestiture of Veritas. He told iTWire that Symantec's unified security strategy has four legs: Machine learning is "finding its way into everything we do," he said. Symantec started applying machine learning more than a decade ago, and now has a team of between 30 and 40 people working to apply it to autonomous anomaly detection. Rather than focusing on detecting specific attributes of malware, spam and so on (which the Bad Guys can learn to work around), the idea is to take a more dynamic approach by examining relationships instead. This give "top of the line" detection of new threats, Swaroop said, with the lowest rate of false positives.

Hanson Robotics touted their android, Sophia, at this year's SWXW in Austin, Texas. The android is arguably the most human like robot to date, capable of masking 62 different facial expressions. Fundamentally, Sophia was designed to be "as conscious, creative and capable as any human," according to to CEO of Hanson Robotics, David Hanson. In addition to her various facial expressions, Sophia can recognize people, hold eye contact, carry on intelligent conversations with speech recognition software and is even capable of learning. Sophia is clothed in a patented silicon skin called Frubber, an elastic form of rubber used in robotics.

Microsoft researchers have come up with a novel way of getting computers to tell stories about what's happening in multiple photographs by using artificial intelligence (AI). Today the company is publishing an academic paper (PDF) describing the technology, which could one day power services that are especially useful to the visually impaired. Microsoft is also releasing the photos, captions, and "stories" developed in the research. The new capability is significant because it goes well beyond just identifying objects in images, or even videos, in order to generate captions. "It's still hard to evaluate, but minimally you want to get the most important things in a dimension. With storytelling, a lot more that comes in is about what the background is and what sort of stuff might have been happening around the event," Microsoft researcher Margaret Mitchell told VentureBeat in an interview.

Why, you may ask, is that news? Twenty years have passed since the IBM computer Deep Blue defeated world chess champion Garry Kasparov, and we all know computers have improved since then. But Deep Blue won through sheer computing power, using its ability to calculate the outcomes of more moves to a deeper level than even a world champion can. Go is played on a far larger board (19 by 19 squares, compared to 8x8 for chess) and has more possible moves than there are atoms in the universe, so raw computing power was unlikely to beat a human with a strong intuitive sense of the best moves.

Inside a large, windowless room in an electronics factory in south Shanghai, about 15 workers are eyeing a small robot arm with frustration. Near the end of the production line where optical networking equipment is being packed into boxes for shipping, the robot sits motionless. "The system is down," explains Nie Juan, a woman in her early 20s who is responsible for quality control. Her team has been testing the robot for the past week. The machine is meant to place stickers on the boxes containing new routers, and it seemed to have mastered the task quite nicely. But then it suddenly stopped working. "The robot does save labor," Nie tells me, her brow furrowed, "but it is difficult to maintain."

Korea has announced plans to invest roughly 100 billion won (approximately 87.2 million) to foster the development of supercomputers in the country, according to the Korea Times. Following the 5-game Go match between Google's AlphaGo programme and Go World Champion Lee Se-dol, there has been a rise in interest in AI within the country. The attention has seemingly prompted the Ministry of Science, ICT, and Future Planning to invest 10 billion won annually for the next 10 years to boost the growth of artificial intelligence, big data, the Internet-of-Things technologies and other emerging industries through supercomputers. Alongside this investment, the government has also committed to bolster its cloud computing credentials, both domestically and internationally. At a recent cloud conference, the ministry announced plans to increase the adoption of cloud computing from 6.4% to 13% over the next 12 months, as well as targeting international growth for Korean cloud computing companies.

This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU. This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.