AI - 'artificial intelligence' - promises to bring revolution to many parts of our lives: Smart assistants, fully robotic workplaces, driverless cars, "fake news" propaganda. As the digital world around us becomes smarter, what are the implications socially & economically? And what does the future really hold for us in a world of AI? This interesting and informative talk is delivered by Sven Latham from Noggin. Sven is a self-confessed data and computer geek, using big data & AI to analyse town centres.

Say the phrase "big data," and people tend to picture the TV show Black Mirror. They imagine a creepy dystopian future in which robot overlords control everything. But those fears are overblown. What people should think of when they think of big data is Netflix or Amazon: personalized recommendations and a customized experience that make it easier and faster for the consumer to find what they're looking for. In fact, you could say that, when it comes to big data, consumers worry about Black Mirror but hope for more Netflix.

Our model, called GPT-2 (a successor to GPT), was trained simply to predict the next word in 40GB of Internet text. Due to our concerns about malicious applications of the technology, we are not releasing the trained model. As an experiment in responsible disclosure, we are instead releasing a much smaller model for researchers to experiment with, as well as a technical paper. GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1] of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than 10X the amount of data. GPT-2 displays a broad set of capabilities, including the ability to generate conditional synthetic text samples of unprecedented quality, where we prime the model with an input and have it generate a lengthy continuation. In addition, GPT-2 outperforms other language models trained on specific domains (like Wikipedia, news, or books) without needing to use these domain-specific training datasets. On language tasks like question answering, reading comprehension, summarization, and translation, GPT-2 begins to learn these tasks from the raw text, using no task-specific training data.

What should companies know about AI and the future of work, and what risks can harm the advantages of artificial intelligence?

We already had a robot that could make fast food burgers. And now we have an artificial intelligence that can take your order for one.

Minimax, as the name suggest, is a method in decision theory for minimizing the maximum loss.

Retailers are deploying artificial intelligence today, and reaping the benefits.

New York's governor, Andrew Cuomo, has ordered two state agencies to investigate a media report that Facebook may be accessing far more personal information than previously known from smartphone users, including health and other sensitive data. The directive to New York's department of state and department of financial services (DFS) came after the Wall Street Journal said testing showed that Facebook collected personal information from other apps on users' smartphones within seconds of them entering it. The WSJ reported that several apps share sensitive user data including weight, blood pressure and ovulation status with Facebook. The report said the company can access data in some cases even when the user is not signed into Facebook or does not have a Facebook account. In a statement, Cuomo called the practice an "outrageous abuse of privacy".