Introduction: Deriving meaningful information out of heap of data is the minimal requirement for any establishment today for its survival & sustenance. There are many terminologies and buzz words related to this area that blurs the meaning leaving people confused, such as Bigdata, Data Ware house (DWH), BI analytics, AI (Artificial Intelligence), Data Science, Machine Learning, Advance Analytics, Deep Learning, Cognitive, Predictive modeling etc. to name a few. I have seen institutions would have team using machine learning to build classifiers but they call the same as AI team, Business Analysts would run some diagnostic analysis using Tableau but are called Data Scientists, sometimes we write conventional code or may be use RPA tool such as blue prism in automating certain portion of business process (e.g. College graduates, job seekers (fresh or lateral), business executives and technologists must make good effort to understand the concept behind various data management subject area (AI, ML, DS, Deep Learning, Cognitive Computing, Statistics, BI, DWH etc.), associated roles (such as data engineers, business analysts, data scientists, ML engineer, Data Modelers, data administrators etc.) and subsequently plan to learn and apply else Industries might lose revenue, CoE & practices blur and frustration creep in between expectation vs reality. We, however, haven't gone that far in data maturity area (Exploration, diagnostic, prediction & prescriptive), hence important to clarify and understand various data subject area before its late.

The most exciting developments of AI in 2018 is AutoML. It automates machine learning process. In January this year, Google released AutoML Vision. Then in July Google launched AutoML for machine translation and natural language processing. Both packages have been used by companies such as Disney in practical applications.

During a blockbuster press event last week, Amazon took the wraps off a redesigned Echo Show, Echo Plus, and Echo Spot, and nine other new other voice-activated accessories, peripherals, and smart speakers powered by Alexa. Also in tow: the Alexa Presentation Language, which lets developers build "multimodal" Alexa apps -- skills -- that combine voice, touch, text, images, graphics, audio, and video in a single interface. Developing the frameworks that underlie it was easier said than done, according to Amazon senior speech scientist Vishal Naik. In a blog post today, he explained how Alexa leverages multiple neural networks -- layered math functions that loosely mimic the human brain's physiology -- to resolve ambiguous requests. The work is also detailed in a paper ("Context Aware Conversational Understanding for Intelligent Agents with a Screen") that was presented earlier this year at the Association for the Advancement of Artificial Intelligence.

The talks at the Deep Learning School on September 24/25, 2016 were amazing. I clipped out individual talks from the full live streams and provided links to each below in case that's useful for people who want to watch specific talks several times (like I do). Please check out the official website (http://www.bayareadlschool.org) Having read, watched, and presented deep learning material over the past few years, I have to say that this is one of the best collection of introductory deep learning talks I've yet encountered. Torch Tutorial (Alex Wiltschko, Twitter) - https://youtu.be/L1sHcj3qDNc 11.

Artificial Intelligence is one of the fastest-growing and most exciting fields in technology today! Knowledge of Deep Learning and Machine Learning is highly valued by companies that are creating cutting-edge technology and professionals with these skills can expect their career to skyrocket in the coming years. Edureka offers certification courses in TensorFlow and Mahout to help you take advantage of the career opportunities in Artificial Intelligence.

Shares of Nvidia Corp. had their best single-day performance since April after an analyst said he saw more than 40% upside for the stock. Evercore's C.J. Muse boosted his price target on the shares to $400 from $300 on Friday, making him the most bullish analyst tracked by FactSet. He thinks the company's deep-learning platform is nearing a major breakthrough and predicts that Nvidia's NVDA, 5.09% stock will continue to post strong gains as the company deepens its artificial-intelligence push. "We view Nvidia as being on the cusp of a tipping point in the company becoming the AI standard platform," Muse wrote. He sees a number of applicable business areas, including gaming, high-performance computing, pro visualization, transportation, health care, and autonomous machinery.

Creatives, take note: Artificial intelligence isn't ready to take your job just yet. Burger King plans to send out a press release Thursday declaring that its newest ad campaign was created entirely by artificial intelligence. With the growing hype around A.I. and the buzz about its use in the industry (we're looking at you, Marcel), one might actually believe the premise, if just for a second. In the release, the home of the Whopper says it is testing a beta version of "a new deep learning algorithm that could give a glimpse into what the future of marketing and communications could look like." The brand's statement claims that BK "decided to use high-end computing resources and big data to train an artificial neural network with advanced pattern recognition capabilities by analyzing thousands of fast-food commercials and competitive reports from industry research."

From beating us at the game of GO, to recreating paintings by Van Gogh, algorithms are all around us. Artificial Intelligence is no longer science fiction as we are witnessing breakthrough after breakthrough. What most of these innovations have in common is that they make use of a technique called "deep learning." Earlier I had the chance to ask world renowned AI experts about their favorite algorithms in general. This time we will focus on deep learning!

Researchers at the Alphabet subsidiary DeepMind have spelled out how they will ensure that AI is developed safely. The guidelines aim to make certain that powerful systems capable of learning and figuring out their own solutions to problems don't start to behave in unexpected and unwanted ways. The big issues: The researchers say the key challenges are specifying the intended behavior of a system in a way that avoids unwanted consequences; making it robust even in the face of unpredictability; and providing assurances, or ways to override behavior if necessary. Erratic behavior: This is a growing area of academic research. There are plenty of often amusing examples of machine-learning systems that have started behaving oddly.

Human action recognition refers to automatic recognizing human actions from a video clip, which is one of the most challenging tasks in computer vision. In reality, a video stream is often weakly-annotated with a set of relevant human action labels at a global level rather than assigning each label to a specific video episode corresponding to a single action, which leads to a multi-label learning problem. Furthermore, there are a great number of meaningful human actions in reality but it would be extremely difficult, if not impossible, to collect/annotate video clips regarding all of various human actions, which leads to a zero-shot learning scenario. To the best of our knowledge, there is no work that has addressed all the above issues together in human action recognition. In this paper, we formulate a real-world human action recognition task as a multi-label zero-shot learning problem and propose a framework to tackle this problem. Our framework simultaneously tackles the issue of unknown temporal boundaries between different actions for multi-label learning and exploits the side information regarding the semantic relationship between different human actions for zero-shot learning. As a result, our framework leads to a joint latent embedding representation for multi-label zero-shot human action recognition. The joint latent embedding is learned with two component models by exploring temporal coherence underlying video data and the intrinsic relationship between visual and semantic domain. We evaluate our framework with different settings, including a novel data split scheme designed especially for evaluating multi-label zero-shot learning, on two weakly annotated multi-label human action datasets: Breakfast and Charades. The experimental results demonstrate the effectiveness of our framework in multi-label zero-shot human action recognition.