Enes Bilgin works as a Principal AI Engineer and a Tech Lead at Microsoft's Autonomous Systems division, focusing on Project Bonsai. He is a machine learning and operations research practitioner and researcher and the author of a recent book "Mastering Reinforcement Learning with Python." He holds an M.S. and a Ph.D. in Systems Engineering from Boston University and a B.S. in Industrial Engineering from Bilkent University. In the past, he worked as a Research Scientist at Amazon and as an Operations Research Scientist at AMD. He also held adjunct faculty positions at the McCombs School of Business at the University of Texas at Austin and at the Ingram School of Engineering at Texas State University.

I recently started an AI-focused educational newsletter, that already has over 125,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. The emergence of trends such as self-driving cars or drones has helped to popularize an area of artificial intelligence(AI) research known as autonomous agents. Conceptually, autonomous agents are AI that builds knowledge in real-time based on the characteristics of their surrounding environment as well as other agents.

Digital assistant such as Alexa, Siri, Cortana or the Google Assistant are some of the best examples of mainstream adoption of artificial intelligence(AI) technologies. These assistants are getting more prevalent and tackling new domain-specific tasks which makes the maintenance of their underlying AI particularly challenging. The traditional approach to build digital assistant has been based on natural language understanding(NLU) and automatic speech recognition(ASR) methods which relied on annotated datasets. Recently, the Amazon Alexa team published a paper proposing a self-learning method to allow Alexa correct mistakes while interacting with users. The rapid evolution of language and speech AI methods have made the promise of digital assistants a reality.

Every week, Invector Labs publishes a newsletter that covers the most recent developments in AI research and technology. You can find this week's issue below. You can sign up for it below. Machine learning literature typically divide the world in supervised and unsupervised models but reality is much more complex. While supervised models rule the current artificial intelligence(AI) solution space, they often result unpractical given their dependency on training data.