Hello, happy to see you here. Today let's dive into some popular topics which are often discussed in machine learning interviews. Without further ado, presenting to you the next cheat sheet. But what exactly do we do when we already have data? We will stop on each task separately in detail in next articles.

As deep neural networks have come to dominate AI, the Conference on Neural Information Processing Systems (NeurIPS) has become the most popular conference in the field. And at the most popular conference in the field, one of the most popular topics is reinforcement learning: at this year's NeurIPS, 95 accepted papers use the term in their titles. "Reinforcement learning is very, very powerful, because you can kind of learn anything, adaptively from the feedback, and by exploring the decision space," says Shipra Agrawal, an Amazon Scholar, an assistant professor in Columbia University's Industrial Engineering and Operations Research Department, and an area chair at NeurIPS, who studies reinforcement learning. "In concept, it's very akin to how humans learn, by trial and error, and how they adapt to what they see -- without requiring a loss function and so on, just by some kind of rewards or positive feedback." In reinforcement learning, an agent explores its environment, trying out different responses to different states of affairs, gradually learning a set of policies that will enable it to maximize some reward.

A Scottish university reached the final three of a prestigious international competition dedicated to advancing conversational artificial intelligence (AI). A team of Phd students from Heriot-Watt saw more than 100 entries from 22 countries including the likes of Harvard and Princeton to become the only UK institution to be placed in the Alexa Prize. A nine-strong team, named What's Up Bot, won plaudists from judges for their artificial intelligence software named Alana, which can understand and respond to human conversation. The annual competition is organised by online retail giant Amazon and is named after the Alexa voice command system that powers Amazon Echo. The team of PhD students from Heriot-Watt's school of mathematical and computer sciences finished behind fellow finalists, the Czech Technical University and eventual winners, the University of Washington, at a ceremony held in Las Vegas on Tuesday, 28 November.

Insurance technology, also known as InsurTech, is an economic industry composed of companies that use technology to make insurance services more efficient. InsurTech is an integral part of the global FinTech revolution to bring more innovation to the financial sector in general. InsurTech involves exploring avenues that large insurance firms perhaps have less incentive to exploit, such as offering highly customized policies, social insurance, and using new streams of data from internet-enabled devices to dynamically price premiums according to observed behaviour. InsurTech attracted around $2.6 billion worth of investment last year, and market research analysts at Technavio predict that the global InsurTech market will grow steadily at a rate of 10% over the next four years. We reached out to some of the top influencers on our list to ask them for their views on InsurTech.

Robotics is the branch of science consisting of a diverse range of technologies including mechanical engineering, electrical engineering and computer science. Robotics covers the design, construction, operation and use of robots, as well as computer systems for their control, sensory feedback and information processing. The aim of robotics is to create interactive, cognitive and automated systems that can extend human capabilities to solve real world challenges. Pre-Programmed Robots - Robots that operate in a simple, controlled environment such as a car factory. Autonomous Robots – Robots that operate independently of human operators and can detect and adapt to changes in the environment.

Artificial intelligence (AI) is becoming ubiquitous. With advances in technology, algorithms, and sheer compute power, it is now becoming practical to utilize AI techniques in many everyday applications including transportation, healthcare, gaming, productivity, and media. Simple and natural for humans, voice communication in everyday language continues to be one of the ultimate challenges for AI. Human conversation requires the ability to understand the meaning of spoken language, relate that meaning to the context of the conversation, create a shared understanding and world view between the parties, model discourse and plan conversational moves, maintain semantic and logical coherence across turns, and to generate natural speech. Today, we are pleased to announce the Alexa Prize, a 2.5 million university competition to advance conversational AI through voice.