Last month, OpenAI, the Elon Musk-founded artificial intelligence research lab, announced the arrival of the newest version of an AI system it had been working on that can mimic human language, a model called GPT-3. In the weeks that followed, people got the chance to play with the program. If you follow news about AI, you may have seen some headlines calling it a huge step forward, even a scary one. I've now spent the past few days looking at GPT-3 in greater depth and playing around with it. I'm here to tell you: The hype is real. It has its shortcomings, but make no mistake: GPT-3 represents a tremendous leap for AI. A year ago I sat down to play with GPT-3's precursor dubbed (you guessed it) GPT-2.

Decision-making module enables autonomous vehicles to reach appropriate maneuvers in the complex urban environments, especially the intersection situations. This work proposes a deep reinforcement learning (DRL) based left-turn decision-making framework at unsignalized intersection for autonomous vehicles. The objective of the studied automated vehicle is to make an efficient and safe left-turn maneuver at a four-way unsignalized intersection. The exploited DRL methods include deep Q-learning (DQL) and double DQL. Simulation results indicate that the presented decision-making strategy could efficaciously reduce the collision rate and improve transport efficiency. This work also reveals that the constructed left-turn control structure has a great potential to be applied in real-time.

Nils Nilsson and Sven Wahlstrom pictured with Shakey, the first humanoid. This is Nils Nilsson calling!!" Or so the booming voice on the other end of the line claimed, as I skeptically held the phone in my hand on a Sunday afternoon mid-way through my final semester as an undergraduate in Toronto. I had been in my dorm room preparing for a mid-term in my Introduction to Artificial Intelligence course, in fact reading through the textbook which Nils himself had authored. I was certain the call was a prank perpetrated by a classmate who knew I was anxiously awaiting word on whether I would be accepted to the PhD program at Stanford. But it turned out to be legitimate: Nils had recently taken on the role as Chair of the Computer Science department at Stanford and decided that he wanted to call all of the applicants that had been offered admission.

In a recruitment industry, selecting a best CV from a particular job post within a pile of thousand CV's is quite challenging. Finding a perfect candidate for an organization who can be fit to work within organizational culture is a difficult task. In order to help the recruiters to fill these gaps we leverage the help of AI. We propose a methodology to solve these problems by matching the skill graph generated from CV and Job Post. In this report our approach is to perform the business understanding in order to justify why such problems arise and how we intend to solve these problems using natural language processing and machine learning techniques. We limit our project only to solve the problem in the domain of the computer science industry.

Huyn Kim is the CEO and Co-Founder of Superb AI, a company that provides a new generation machine learning data platform to AI teams so that they can build better AI in less time. The Superb AI Suite is an enterprise SaaS platform built to help ML engineers, product teams, researchers and data annotators create efficient training data workflows. What initially attracted you to the field of AI, Data Science and Robotics? As an undergraduate majoring in Biomedical Engineering at Duke, I was passionate about genetics and how we can engineer our DNA to cure diseases or create genetically engineered organisms. I remember one wet-lab experiment distinctly that kept failing for like 6 months straight. The most frustrating part of it was that there was a lot of repetitive manual work, and in hindsight that was probably the root of some many potential errors.

Every feature of intelligence or learning aspects in principle can be so precisely described that a machine can seamlessly simulate it. John McCarthy, who is the Father of Artificial Intelligence, was a pioneer in the fields of AI. He not only is credited to be the founder of AI, but also one who coined the term Artificial Intelligence. In 1955, John McCarthy coined the term Artificial Intelligence, which he proposed in the famous Dartmouth conference in 1956. This conference attended by 10-computer scientists, saw McCarthy explore ways in which machines can learn and reason like humans.

Chief Marketing Officer at Interactions, a conversational AI company, where he oversees all aspects of communications, sales and marketing. Let's face it: When a company develops artificial intelligence (AI) that can offer us a medical diagnosis, care for our elderly grandparents or autonomously drive a vehicle, ethics aren't the flashiest elements to focus on. It's tempting for companies to get caught up in the excitement of creating the latest cutting-edge technology and vow to sort out ethical considerations after the fact. That works just as well, right? Late last year, I had a conversation with Thomas Arnold, a research associate at Tufts' Human-Robot Interaction Lab, for my company's podcast.

I work for Icon Solutions. We work in instant payments. What I want to talk about is applying machine learning to fraud detection. When we first started researching it, we found two themes that were going on. We found these hype type things. I'm sure you've all seen this, when will we bow to our machine overlord? By 2025, robots will be playing symphonies and all that stuff. Then we found the other extreme as well, which was the fairly wacky math. What we were looking at is how can we actually apply this technology to our requirements and to those of our clients? I'm going to talk about payments. Then I'm going to do a demonstration. In terms of payments, the way it worked, if you wanted to interact with the bank through most of 20th centuries, you had to go into a branch. That was the only way you could interact with the bank. If somebody wanted to steal money from a bank, they had to rob it. That was basically the only option they had, which is why you can see the big security barriers that they had in the branches at that point in time. Then, moving on to about 1960s, the bank started employing new technologies. They took things like the IBM 360 series, and they actually started using it. Even then it was pretty secure. The people who were using it were people who worked for the bank. It was a closed network. If you wanted to actually get into the systems, you had to go into the bank's offices, and you had to be an employee. The potential for fraud was fairly small.

Among the many departments and agencies within the United States federal government, the US Department of Energy (DOE) stands out as one of the most science, technology, and innovation-focused. This should come as little surprise to those who know the DOE's storied history with its breakthrough labs, world-leading research institutions, and highly educated staff. Since World War II, the DOE has been at the forefront of most of the groundbreaking and world-changing revolutions in science and technology including the development and harnessing of nuclear energy, innovations in genomics including the DOE initiative Human Genome Project, work in high-performance computing, and many other research-oriented efforts. In fact, the DOE supports more research in the physical sciences than any other US federal agency, providing more than 40% of US funding in computing, physics, chemistry, materials science, and other area through a system of national laboratories including Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory, Argonne National Laboratory, Ames Laboratory, Brookhaven National Laboratory, Los Alamos National Laboratory, Sandia National Labs, Lawrence Livermore National Laboratory, the SLAC National Accelerator Laboratory, and dozens more institutions. Until very recently, the DOE also ran the world's top two fastest supercomputers: Summit and Sierra.

RPA is fast becoming the destination for efficient and more productive resources, what do you think is the future of human intelligence? Processes that are easily defined, repeatable and rules-based will all ultimately be automated. As to what happens to human intelligence – that's a great question with no short answer. As AI and cognitive computing continue to improve and expand, there will inevitably come an inflexion point, when the super-smart machine will surpass our mere mortal intelligence. The question I have is whether intelligence and knowledge may soon be ubiquitous and hence commoditized?