To some, the phrase "artificial intelligence" conjures nightmare visions: something out of the '04 Will Smith flick I, Robot, perhaps, or the ending of Ex Machina when a boot smashes through the glass of a computer screen to stamp on a human face, forever. Even people who study AI have a healthy respect for the field's ultimate goal, artificial general intelligence, or an artificial system that mimics human thought patterns. Computer scientist Stuart Russell, who literally wrote the textbook on AI, has spent his career thinking about the problems that arise when a machine's designer directs it toward a goal without thinking about whether its values are all the way aligned with humanity's. A number of organizations have sprung up in recent years to combat that potential, including OpenAI, a working research group that was founded (then left) by techno-billionaire Elon Musk to "to build safe [AGI], and ensure AGI's benefits are as widely and evenly distributed as possible." What does it say about humanity that we're scared of general artificial intelligence because it might deem us cruel and unworthy and therefore deserving of destruction?

Artificial Intelligence (AI) is being used for everything from facial recognition software to chat bots that interact with a company's potential customers. Some researchers want to discover what happens when they give their neural networks a chance to show their creative side -- and the results are surreal to say the least. AI researcher Robbie Barrat fed a Generative Adversarial Network (GAN) thousands of nude portraits from a dataset and then trained it to create its own version of the artwork it processed. The results of the AI art of surreal, swirly naked female forms would impress even the likes of Salvador Dali. Generative adversarial networks are artificial intelligence algorithms that are used in unsupervised machine learning.

Data analysis is a process for inspecting, consolidating, transforming, and making sense of data in a way that guides the decision-making process. Scala has emerged as an important tool for efficiently performing various data analysis tasks. So, if you're interested to load raw datasets with Spark, and perform exploratory data analysis on those via plotting Scala libraries, then go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Let's take a quick look at your learning journey.

It's no surprise, then, that AI engineers continue to sit at the focus of Nvidia's human resources. A little deeper digging can reveal what exactly those engineers will be focusing on and the particular skillsets they're investing in, which can indicate where the tech giant's strategy is heading next. This is particularly key for newcomers in the space who can be more agile and disruptive. There's a major focus on AI chips right now, with startups like Hailo, who has raised $16M for their deep learning chip, continuing to attract investor attention. Incumbents like Nvidia will need to invest heavily in additional talent in order to remain competitive in these areas.

And that raises an obvious question to those in the video industry: What can artificial intelligence do in this sector? The answers appear to span the gamut: Advances in machine learning, deep learning and artificial intelligence could well aid users in discovering new content, or help providers discover new ways to monetize archived videos. Artificial intelligence could even play a role in the creation of video content through topic selection, editing, monitoring and analytics.

The introduction of AI and machine learning should not mean a decision of man or machine, but one of man and machine bringing combined skills together. Speaking at Infosecurity Europe 2018, Christopher Morales, head of security analytics at Vectra Networks, looked at'Building Security That Works, Machine Learning Fundamentals for Cybersecurity Professionals' admitted that there is confusion around what AI, machine learning and deep learning are. "AI is the output of what you're trying to do, and do things that is a repetitive task," he said. "Machine learning is the method and the means to AI, but it is not AI itself." Morales went on to say that deep learning is part of machine learning, and there are two types: supervised or unsupervised.

In the finance sector, for instance, AI and ML can help firms improve everything from their retail banking experience to their trading algorithms, business analytics and fraud detection protocols. But there's a catch: the promise of AI and ML technologies may not be realised unless some key enabling technologies are put in place at the outset. First, firms need a network with fast, scalable, secure and rapidly deployable connectivity. Second, they need a way to coordinate big data as it flows between public and private clouds. This need for smart connectivity stems from the special demands of artificial intelligence.

The Internet has been exceptionally useful for finding what we're looking for, and as companies continue to use emerging technologies for better customer experience, it becomes easier and faster than ever. Digital asset company Shutterstock is expanding how people can use its services to find pictures and illustrations that fit their needs with three new search tools. Shutterstock Reveal is a new Google Chrome extension that allows users to select an image on a page and search Shutterstock for similar images. Copy Space lets users search for photos that allow them to include space for the desired amount of copy. Refine is a new option where users can select which results from the first page best match their inquiry and have the site re-evaluate its results for a better fit.

We find 6 tools form the modern open source Data Science / Machine Learning ecosystem; examine whether Python declared victory over R; and review which tools are most associated with Deep Learning and Big Data.

Monte-Carlo Tree Search (MCTS) has been found to show weaker play than minimax-based search in some tactical game domains. This is partly due to its highly selective search and averaging value backups, which make it susceptible to traps. In order to combine the strategic strength of MCTS and the tactical strength of minimax, MCTS-minimax hybrids have been introduced, embedding shallow minimax searches into the MCTS framework. Their results have been promising even without making use of domain knowledge such as heuristic evaluation functions. This article continues this line of research for the case where evaluation functions are available. Three different approaches are considered, employing minimax with an evaluation function in the rollout phase of MCTS, as a replacement for the rollout phase, and as a node prior to bias move selection. The latter two approaches are newly proposed. Furthermore, all three hybrids are enhanced with the help of move ordering and k-best pruning for minimax. Results show that the use of enhanced minimax for computing node priors results in the strongest MCTS-minimax hybrid investigated in the three test domains of Othello, Breakthrough, and Catch the Lion. This hybrid, called MCTS-IP-M-k, also outperforms enhanced minimax as a standalone player in Breakthrough, demonstrating that at least in this domain, MCTS and minimax can be combined to an algorithm stronger than its parts. Using enhanced minimax for computing node priors is therefore a promising new technique for integrating domain knowledge into an MCTS framework.