Siri on the iPhone, Cortana on Windows, Alexa, self-driving cars, and face recognition on Facebook photos, these are all examples of how Artificial Intelligence (AI) is already having a profound impact on human lives. This trend is also impacting manufacturing, albeit on a smaller scale now, with robots being increasingly used to replace humans in factories across the globe, from the US, Europe, Japan, China and Taiwan. Foxconn has already replaced 60,000 humans with robots in its factories, after developing artificial intelligence solutions for its manufacturing processes. Such a strategy they insist shall help them offset increasing labour costs in labour-intensive countries such as China. Also, with AI technologies finding their way into manufacturing, leading economists from MIT are worried that technology is replacing jobs at a faster pace than creating new ones, which could lead to higher unemployment over a short-term period.

These days, even when I read news about India's state owned air carrier, as "New AI connectivity between cities X & Y", the first connection my (poor human) brain makes is to'Artificial Intelligence'. Only then it connects to Air India. This is because everyone (in my extended professional circle) is talking of Artificial Intelligence. Though I had done a short elective on AI in my computer engineering days (16 years back), done some basic LISP programming as part of it, had presented a paper on'Genetic Algorithms' in a seminar (again that long ago), and am an avid watcher of Sci-Fi movies around AI (and claim to have understood Matrix the first time I watched it -- with subtitles though, ha!), I realize that my knowledge of what AI is, is no better than a layperson (or worse, since half knowledge is more dangerous). This article is an endeavor to sort of unpack AI (and the associated words like ML -- machine learning, DL -- deep learning) for myself. And publishing it around to benefit others that are in a similar boat. And also to get feedback from others to correct my understanding. Without much dumbing down (or may be with), Artificial Intelligence is fundamentally anything that a computing machine does. Even the basic accountant calculator that does 2 2 4 is'artificial intelligence'. However, we do not consider them as'AI' because of what is called'AI effect' -- When we know'how' a machine does something'intelligent,' it ceases to be regarded as intelligent. Tools we take for granted (pick any of your favorite app) are all'AI'. Anything that is'eaten by software' can be considered as Artificial Intelligence. And why is everyone talking about it?

When I was a young boy I saw the classic movie "2001 A Space Odyssey" with HAL, the voice interactive computer system that bordered on AI, and that sparked in me, a lifelong interest and career in IT. Today we are seeing devices that are starting to provide the beginnings of that same functionality like the Amazon Echo, Dot, or Google Home. It's one thing to sit in your living room and call out to the air "Alexa, how old is Matt Damon" or "Alexa, play'The Logical Song' by Supertramp", it's another when your 6 year old is having a conversation with Alexa and orders a bunch of things from Amazon and it is quite another when you are trying to find ways to use it in the office to make your company more productive. The difference between Alexa and HAL is pretty dramatic, but at the core of them both, and AI in general, is Machine Learning. As Guy Levy-Yurista, Sisense Head of Product, described in this recent blog post: "Sisense employs machine learning as a core element of its In-Chip data processing algorithms….We call it query recycling – breaking queries into smaller blocks that are later reassembled to answer future queries: if user A asks a completely new question such as'what was our average deal size last year?' and user B later asks'what is our year-over-year growth in sales?', This isn't OLAP, it is a learning algorithm that grows smarter and more efficient over time and as more unique queries accumulate. It learns to identify the reusable chunks within each query, and to use these as a knowledge base for future reference".

The concept of artificial intelligence (AI) has always fascinated humans. The ancient Greek myths of Hephaestus and Pygmalion incorporated the idea of intelligent robots and artificial beings. In the 1950s, British scientist Alan Turing proposed the now-famous Turing Test as a measure of machine intelligence, and Isaac Asimov published his Three Laws of Robotics. The Turing Test has been long passed, and AI is an everyday occurrence with Siri and Google Now. At the same time SMS messaging has become the most popular method of written communication in history, with an estimated 6 billion humans now using mobile messaging to talk to each other around the world.

Tinder could become a pushy matchmaker. Rather than simply present users with profiles to "like" or discard, future versions of the dating app will suggest that you take Jessica from No 32 to an indie gig at 8pm tomorrow. The app already uses an algorithm to suggest matches based on things such as location and common interests but its creators plan to use artificial intelligence to suggest specific dates. The company has launched a division to buy tech start-ups that will enable this change. It wants people to use the smarter Tinder with voice-activated digital assistants such as Apple's Siri.

Many modern commercial sites employ recommender systems to propose relevant content to users. While most systems are focused on maximizing the immediate gain (clicks, purchases or ratings), a better notion of success would be the lifetime value (LTV) of the user-system interaction. The LTV approach considers the future implications of the item recommendation, and seeks to maximize the cumulative gain over time. The Reinforcement Learning (RL) framework is the standard formulation for optimizing cumulative successes over time. However, RL is rarely used in practice due to its associated representation, optimization and validation techniques which can be complex. In this paper we propose a new architecture for combining RL with recommendation systems which obviates the need for hand-tuned features, thus automating the state-space representation construction process. We analyze the practical difficulties in this formulation and test our solutions on batch off-line real-world recommendation data.

Harriet Green, GM, Watson IoT, Commerce & Education and Mona Abutaleb, SVP of Services, Ricoh Americas with the voice controlled interactive whiteboard in Munich, Germany. Imagine if the whiteboard in your next corporate meeting could take notes when you talked and add comments from your teammates in the meeting. IBM and Ricoh Europe have announced an interactive whiteboard with artificial intelligence (AI) that puts cognitive computing right in the middle of a meeting room. The IBM Watson-powered whiteboard looks like any other run-of-the-mill meeting room whiteboard. Anyone in the meeting room or remotely joining by conference call, can control what's on the whiteboard with voice commands.

Samsung is apparently ready to bet big on artificial intelligence, with a massive amount of money set aside for buying more AI companies. Artificial intelligence is rapidly gaining ground and it's playing an increasingly bigger role in people's everyday lives. From smart digital assistants to all sorts of gadgets, robots, infotainment systems and more, AI is shaping up as a cornerstone of modern technology and it adds convenience in virtually every technology area. We've seen an impressive growth in the artificial intelligence field in recent years and the trend is gaining momentum still, with more companies embedding AI technology into more products and areas. Samsung itself is expected to take a big AI plunge with a smart assistant called Bixby, presumably set to grace the next-generation Samsung Galaxy S8 flagship.

Women have been made into servants once again. Apple's Siri, Amazon's Alexa, Microsoft's Cortana, and Google's Google Home peddle stereotypes of female subservience--which puts their "progressive" parent companies in a moral predicament. People often comment on the sexism inherent in these subservient bots' female voices, but few have considered the real-life implications of the devices' lackluster responses to sexual harassment. By letting users verbally abuse these assistants without ramifications, their parent companies are allowing certain behavioral stereotypes to be perpetuated. Everyone has an ethical imperative to help prevent abuse, but companies producing digital female servants warrant extra scrutiny, especially if they can unintentionally reinforce their abusers' actions as normal or acceptable. In order to substantiate claims about these bots' responses to sexual harassment and the ethical implications of their pre-programmed responses, Quartz gathered comprehensive data on their programming by systematically testing how each reacts to harassment. The message is clear: Instead of fighting back against abuse, each bot helps entrench sexist tropes through their passivity. And Apple, Amazon, Google, and Microsoft have the responsibility to do something about it.

The concept of artificial intelligence (AI) has always fascinated humans. The ancient Greek myths of Hephaestus and Pygmalion incorporated the idea of intelligent robots and artificial beings. In the 1950s, British scientist Alan Turing proposed the now-famous Turing Test as a measure of machine intelligence, and Isaac Asimov published his Three Laws of Robotics. The Turing Test has been long passed, and AI is an everyday occurrence with Siri and Google Now. At the same time SMS messaging has become the most popular method of written communication in history, with an estimated 6 billion humans now using mobile messaging to talk to each other around the world.