History suggests that the worst fears of machines making humans obsolete don't come true. In some quarters, AI and robotics are considered the Fourth Industrial Revolution, following other big transformations in the 18th, 19th and 20th centuries. And while displacement of jobs occurred in each wave of new technology, new jobs emerged to balance out some of the pain. Concern about technology-driven mass unemployment "has proven to be exaggerated" throughout history, University of Oxford academics Carl Benedikt Frey and Michael Osborne wrote in an influential 2013 paper. Rather, technological progress "has vastly shifted the composition of employment, from agriculture and the artisan shop, to manufacturing and clerking, to service and management occupations," they wrote.

The next time you get sick, your care may involve a form of the technology people use to navigate road trips or pick the right vacuum cleaner online. Artificial intelligence is spreading into healthcare, often as software or a computer program capable of learning from large amounts of data and making predictions to guide care or help patients. It already detects an eye disease tied to diabetes and does other behind-the-scenes work like helping doctors interpret MRI scans and other imaging tests for some forms of cancer. Now, parts of the health system are starting to use it directly with patients. During some clinic and telemedicine appointments, AI-powered software asks patients initial questions about their symptoms that physicians or nurses normally pose.

Some believe it will present us with innovative solutions to a myriad of problems, others believe it will signal the demise of all mankind. In an effort to learn more about the current state of this exciting technology, and cut through the noise, I jumped at the opportunity to listen and learn from Galina Alperovich, senior machine learning researcher at Avast, during the Digital Transformation EXPO Europe, currently in its 15th year of congregating the most influential innovators and practitioners from around the globe. Avast uses a network comprised of hundreds of millions of sensors (their end-users) that funnel data into the company's A.I. system, in order to detect and defend against cyber threats. "For cybersecurity, A.I. is super important," according to Ondrej Vlcek, chief executive officer of Avast. "A.I. is a great tool for cybersecurity to provide better defense and protection against attacks."[1]

The first book is posted on data science central here, and the community group is here. Please join the community so you can also access the other'In a weekend' books It is also associated with a diverse range of people including Golf (Ben Hogan), Shaolin Monks, Benjamin Franklin etc. This means we don't need any installation (it's completely web-based) We will guide you through two end-to-end machine learning problems that can be taken over one weekend. We will introduce you to important machine learning concepts, such as machine learning workflow, defining the problem statement, pre-processing and understanding our data, building baseline and more sophisticated models, and evaluating models. We will also introduce to keep machine learning libraries in python and demonstrate code that can be used on your own problems.

In 2018 we saw a number of landmark research breakthroughs in the field of natural language processing (NLP). The introduction of transfer learning and pretrained language models in NLP pushed forward the limits of language understanding and generation. These also dominated NLP progress this year. Teams from top research institutions and tech companies explored ways to make state-of-the-art language models even more sophisticated. Many improvements were driven by massive boosts in computing capacities, but many research groups also discovered ingenious ways to lighten models while maintaining high performance. In this article, we summarize 11 research papers covering key language models presented during the year as well as recent research breakthroughs in machine translation, sentiment analysis, dialogue systems, and abstractive summarization.

The dawn of truly artificial intelligences will provoke an international security crisis, according to F-Secure chief research officer and security industry heavyweight, Mikko Hypponen. Speaking to Computer Weekly in October 2019 during an event at the company's Helsinki headquarters, Hypponen said that although true AI is a long way off – in cyber security it is largely restricted to machine learning for threat modelling to assist human analysts – the potential danger is real, and should be considered today. "I believe the most likely war for superhuman intelligence to be generated will be through human brain simulators, which is really hard to do – it's going to take 20 to 30 years to get there," said Hypponen. "But if something like that, or some other mechanism of generating superhuman levels of intelligence, becomes a reality, it will absolutely become a catalyst for an international crisis. It will increase the likelihood of conflict."

Telecom company Bharti Airtel on Monday announced the'acquihire' of Gurgaon based Quikmile, a start up focused on building tech-enabled logistics platforms for India. The company did not disclose the size of the'acquihire' deal, which typically entails acquisition of people or teams. "Quikmile's team will now be a part of Airtel X Labs – Airtel's digital innovation factory, which focuses on IoT, Digital Engineering, Artificial Intelligence, and Machine Learning," Airtel said in a statement. Airtel X Labs team comprises of professionals from India and abroad, the statement said, adding that it is building advanced digital capabilities to serve the emerging needs of Digital India and is rapidly scaling up its teams and scope of work. Quikmile, which started operations in 2018, aims to harness the power of digital technology to transform India's highly disorganized logistics sector and bring efficiency, quality, and safety to fleet owners.

In reality, there is nothing artificial about these algorithms or their intelligence, and the term "AI" is a mystification! The term that describes the reality is "Human-Trained Machine Learning", in today's mad scramble to train these algorithms to mimic human intelligence and brain functioning. In the techie magazine WIRED, October 2018, we meet a pioneering computer scientist, Fei-Fei LI, testifying at a Congressional hearing, who underlines this truth. She said, "Humans train these algorithms" and she talked about the horrendous mistakes these machines make in mis-identifying people, using the term "bias in--bias out" updating the old computer saying, "garbage in--garbage out". Professor LI described how we are ceding our authority to these algorithms to judge who gets hired, who goes to jail, who gets a loan, a mortgage or good insurance rates -- and how these machines code our behavior, change our rules and our lives.

Facebook is under fire for privacy concerns once again, as the social media giant tested a facial recognition app on its employees. Using real-time facial recognition, the firm was able to identify a person by pointing a smartphone camera at them. It was reported that the app has been discontinued, but the technology was capable of bringing up someone's Facebook profile who had enabled facial recognition on their profiles. Facebook did confirm that it developed the app, but denied it was capable of identifying members of its social media network and pulling up their profile. Facebook is under fire for privacy concerns once again, as the social media giant revealed it tested a facial recognition app on its employees.

This week I attended my first developer conference as a data scientist. It was a great experience to meet people, be exposed to new ideas and increase my motivation to keep learning. I'd like to share my experiences as well as a few things I learned. The conference I attended was the LINE Developer Day 2019. It was held on the 20th and 21st of November in Tokyo.