AI is arguably the biggest tech topic of 2018. From Google Duplex's human imitations and Spotify's song recommendations to Uber's self-driving cars and the Pentagon's use of GoogleAI, the technology seems to offer everything to everyone. You could say AI has become synonymous with progress via computing. However, not all AI is created equal, and for AI to fulfill its many promises, it needs to be creative. Let's start by addressing what I mean by "creative."

Up to now, deep learning methods in RL used multiple function approximators (typically a network with shared hidden layers) to fit a state value or state-action value distribution. For instance, bootstrappedDQN () used k-heads on the state-action value function Q for every available action and used it to model a distribution. In bayesianpol (), a Bayesian framework was applied to the actor-critic architecture by fitting a Gaussian Process (GP) instead of the critic, hence allowing for a closed-form derivation of update rules. More recently, bellemare2017distributional () introduced a distributional algorithm C51 which aimed to solve the RL problem by learning a categorical probability vector over returns Q. Unlike GANRL () which uses a generative network to learn the underlying transition model of the environment, we utilize a generative network to model the distribution approximation of the Bellman updates.

As world population grows, crop production needs to keep up. Can we use Artificial Intelligence for Agriculture? Right now, AI is being (and will be) used for so many things. If you follow journals, blogs, publications, and more, you can see people solving problems from speech recognition to breast cancer detection and much more. So, why not try to solve a problem for the agricultural space. I know you may be thinking, "What?", but actually, AI -- and more specifically, Deep Learning -- can be used for this purpose as well.

Intel's Gadi Singer believes his most important challenge is his latest: using artificial intelligence (AI) to reshape scientific exploration. In a Q&A timed with the first Intel AI DevCon event, the Intel vice president and architecture general manager for its Artificial Intelligence Products Group discussed his role at the intersection of science--computing's most demanding customer--and AI, how scientists should approach AI and why it is the most dynamic and exciting opportunity he has faced. How is AI changing science? Scientific exploration is going through a transition that, in the last 100 years, might only be compared to what happened in the '50s and '60s, moving to data and large data systems. In the '60s, the amount of data being gathered was so large that the frontrunners were not those with the finest instruments, but rather those able to analyze the data that was gathered in any scientific area, whether it was climate, seismology, biology, pharmaceuticals, the exploration of new medicine, and so on.

IBM has been on a roll lately with POWER Systems. Principal analyst and colleague Patrick Moorhead wrote about Google deploying POWER9 announced at the OpenPOWER Summit and last week I wrote about IBM expanding its platform HCI footprint with Nutanix. As another output of a long partnership, IBM and Red Hat recently announced the availability of IBM Cloud Private on Red Hat OpenShift/ Red Hat Enterprise Linux (RHEL). Additionally, IBM recently announced the availability of PowerAI for Red Hat Enterprise Linux (RHEL) running on IBM POWER9 platforms. What does this mean and how does this impact the enterprise IT organization?

When Devaki Raj, the founder of the Mountain View-based deep learning startup CrowdAI, and Inc. 30 under 30 alum, learned that an artificial intelligence engineer she was trying to recruit was in the hospital, she didn't think twice about what she did next: Raj showed up at the hospital with flowers and balloons. She wanted to make sure the candidate got the message that her startup would be a very different atmosphere than what he'd get at a larger company. There's a talent war brewing between startups and big companies as both scramble to find top-notch artificial intelligence experts in a relatively small labor pool. "There's just not enough talent out there," Raj says. The number of experts in this field is not clear--Montreal startup Element A.I., which helps businesses build machine learning teams, estimates that there are some 20,000 PhD-level scientists around the world capable of building A.I. systems.

The online fashion retail space continues to boom, as modern consumers increasingly lean towards a more convenient remote buying experience. However, to stay ahead of the competition, leading retail brands need to offer an ever evolving catalogue of seasons, styles and sizes on their platforms. This poses a challenge for inventory management, when constantly updating and removing available items daily. For warehouse staff, and catalogue managers, one of the biggest challenges is developing an efficient system to log new items while on the move, and quickly determine whether the item is already in stock. When dealing with such large catalogues, the traditional method of manually checking each garment from millions of items, would slow processes to a snail's pace.

Andrew Ng is a hero and a role model for everyone who is starting the machine learning journey. One of his earliest Machine Learning courses saw lakhs of students enrolling and getting a huge boost to their careers. He is now back with a course in Deep Learning specialisation supported by his company Deeplearning.ai. Andrew Ng, one of the foremost artificial intelligence experts, is working hard to train more AI experts on a larger scale who can work across a range of industries. Ng has been an early adopter of online learning with the creation of Coursera.

The Intel AI Lab has open-sourced a library for natural language processing to help researchers and developers give conversational agents like chatbots and virtual assistants the smarts necessary to function, such as name entity recognition, intent extraction, and semantic parsing to identify the action a person wants to take from their words. Just a few months old, the Intel AI Lab plans to open-source more libraries to help developers train and deploy artificial intelligence, publish research, and reproduce the latest innovative techniques from members of the AI research community in order to "push AI and deep learning into domains it's not a part of yet." "We would like to contribute this back to the open source community so that either as a beginner or as an engineer or researcher you can look at what with reproduce and investigated and verified and then use it for your own purpose," Intel AI Lab head of data science Yinyin Liu told VentureBeat in an interview at Intel AI DevCon. The first-ever conference by Intel for AI developers is being held Wednesday and Thursday, May 23 and 24, at the Palace of Fine Arts in San Francisco. The Intel AI Lab now employs about 40 data scientists and researchers and works with divisions of the company developing products like the nGraph framework and hardware like Nervana Neural Network chips, Liu said.

The concept of artificial intelligence (AI) is definitely not a new one. For most of us, our first encounter was through the science fiction (Sci-fi) movies. We have been gripped by The Terminator series, The Matrix, I. Robot, Ex Machina, all depicting the amazing imagination of humans to innovate and create machines that can analyze information, solve problems, reason, and function even more efficiently than humans. Though we might not have attained the level of artificial intelligence displayed in these movies, AI is very much a part of our lives today even though we might not be aware. It influences our work, entertainment, and leisure.