Today our focus has been on KPIs, ERP, WMS, TMS, YMS, EDI, The Cloud, S and OP, 3 D Printing, IoT, IoE, Drones: Same Hour/Day/Time Delivery to Customers, Cyber Security, Theft, Government Regulations, E-Commerce, Omni-Channel, Modeling/Simulation, Risk Management, Tracking, Traceability, Re-shoring, Robotics, et al, but…what about Artificial Intelligence or AI technologies? AI is a controversy of deep, lasting dimensions. Will machines learn to think like humans…and then outthink us? The application of AI technologies has created the ability to understand, store and use product information in an entirely new way. AI technologies allow you to understand the underlying grammatical structure, the product DNA, used to build each product, together with all natural variations that potentially exist.

When the tech giant Microsoft unveiled its AI or artificial intelligence-powered bot on Twitter for a playful chat with the people in March, little did the tech giant realise that the twitterati would begin slamming the innocent bot with racist and offensive comments. Launched as an experiment in "conversational understanding" and to engage people through "casual and playful conversation", Tay was soon taken off Twitter by Microsoft engineers. This was a soft experiment. But what if you can interact with a "chatbot" and send the AI-powered machine your financial requirements like you would text to your banker or chartered accountant in the near future? Facebook wants this to happen and at its F8 global developer conference in April, the social networking giant unveiled AI bots right into its popular messaging app Messenger -- to allow 900 million monthly active users on Messenger to interact with businesses and get updates from them.

Lenovo, reigner in producing personal computer, is now putting its hands on investing 500 million dollar in hardware technology startups after its announcement of new fund. Lenovo has a record of making skilful investment with its first investment of 100 million invested in 30 companies which focused on security, games and smart home devices. Naming the few from the list: Israeli facial recognition startup Face which is listed as Chinese firm iDreamsky; biometric whiz Nok Nok Labs from US and SmartX, biometric facial and fingerprint recognizing attendance system from India. Following the Chinese government propaganda of Made in China 2025 initiative, which is putting prime significance to robotics and Artificial Intelligence, Lenovo will be utilizing this fund to support companies which potentiate its businesses and, in particular, those in the big data, cloud computing, artificial intelligence, robots and other Internet services. Lenovo is not the first to endeavour in this field in china.

Ever find yourself in an endless game of being put on hold and pushing numbers to try to reach a certain department on a customer service call? Ever find yourself throwing your hands up in frustration and shouting, "WHY CAN'T I JUST TALK TO A REAL PERSON?!" This sentiment is coloring the way we use the internet. As messaging apps make their way onto more mobile devices and brands follow marketing trends with their own tools like Facebook Messenger for Business, searching for terms and clicking through multiple menus to find what we're looking for feels increasingly passé every day. It's hard to know which came first--advanced chatbot technology or the mounting use of chat-based apps--but it's clear that we're at an inflection point. What role will chatbots have in serving consumers, and how does their adoption affect brands' current methods of reaching people?

An unclassified 2016 Department of Defense (DoD) document, the Human Systems Roadmap Review, reveals that the US military plans to create artificially intelligent (AI) autonomous weapon systems, which will use predictive social media analytics to make decisions on lethal force with minimal human involvement. Despite official insistence that humans will retain a "meaningful" degree of control over autonomous weapon systems, this and other Pentagon documents dated from 2015 to 2016 confirm that US military planners are already developing technologies designed to enable swarms of "self-aware" interconnected robots to design and execute kill operations against robot-selected targets. More alarmingly, the documents show that the DoD believes that within just fifteen years, it will be feasible for mission planning, target selection and the deployment of lethal force to be delegated entirely to autonomous weapon systems in air, land and sea. The Pentagon expects AI threat assessments for these autonomous operations to be derived from massive data sets including blogs, websites, and multimedia posts on social media platforms like Twitter, Facebook and Instagram. The raft of Pentagon documentation flatly contradicts Deputy Defense Secretary Robert Work's denial that the DoD is planning to develop killer robots.

IS BIG DATA TAKING US CLOSER TO THE DEEPER QUESTIONS IN ARTIFICIAL INTELLIGENCE? What I'm worried about and what I'm thinking about these days is if we're really making progress in AI. I'm also interested in the same kind of question in neuroscience, which is that we feel like we're making progress, but are we? There's huge progress in AI, or at least huge interest in AI--a bigger interest than there's ever been in my lifetime. I've been interested in AI since I was a little kid trying to program computers to play chess, and do natural language databases, and things like that, though not very well. I've watched the field and there have been ups and downs. There were a couple of AI winters where people stopped paying attention to AI altogether. People who were doing AI stopped saying that they were in the field of AI. They say, "Yes, I do artificial intelligence," where two years ago they would have said, "I do statistics." Even though there's a lot of hype about AI and a lot of money being invested in AI, I feel like the field is headed in the wrong direction. There's been a local maximum where there's a lot of low-hanging fruit right now in a particular direction, which is mainly deep learning and big data. People are very excited about the big data and what it's giving them right now, but I'm not sure it's taking us closer to the deeper questions in artificial intelligence, like how we understand language or how we reason about the world. The big data paradigm is great in certain scenarios. One of the most impressive advances is in speech recognition. You can now dictate into your phone and it will transcribe most of what you say right most of the time. That doesn't mean it understands what you're saying. Each new update of Siri adds a new feature. First, you could ask about movie times, then sports, and so forth. The natural language understanding is coming along slowly. You wouldn't be able to dictate this conversation into Siri and expect it to come out with anything whatsoever.

Back in October 2014, Taylor Swift opened an account on Line, the messaging app that is massively popular in Asia. The account currently doesn't do much. If you communicate with it, you can hear a funny voice message from Swift, for instance. Paul McCartney has one, too. Burberry and Selfridges also have accounts on WeChat and Line.

Machine learning has invaded and challenged traditional methods in unimaginable ways in the recent past. Continuing in the same vein, today we look at some of the innovative ways machine learning is addressing alcohol drinking related issues. Be it drunken outburst on social media or drunken driving, new age algorithms are preparing to help you save both your life as well as your reputation. Detecting outbursts on social media More than harmful, drunk tweeting or social media updates are regretful. And a group of researchers at the University of Rochester have been building a machine learning tool just to address this issue.

In experiments involving a simulation of the human esophagus and stomach, researchers at MIT, the University of Sheffield, and the Tokyo Institute of Technology have demonstrated a tiny origami robot that can unfold itself from a swallowed capsule and, steered by external magnetic fields, crawl across the stomach wall to remove a swallowed button battery or patch a wound. The new work, which the researchers are presenting this week at the International Conference on Robotics and Automation, builds on a long sequence of papers on origami robots from the research group of Daniela Rus, the Andrew and Erna Viterbi Professor in MIT's Department of Electrical Engineering and Computer Science. "It's really exciting to see our small origami robots doing something with potential important applications to health care," says Rus, who also directs MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). "For applications inside the body, we need a small, controllable, untethered robot system. It's really difficult to control and place a robot inside the body if the robot is attached to a tether."

Artificial neural networks (ANNs) are a type of information processing system based on mimicking the principles of biological brains, and have been broadly applied in application domains such as pattern recognition, automatic control, signal processing, decision support systems and artificial intelligence. Spiking neural networks (SNNs) are a type of biologically inspired ANN that perform information processing based on discrete time spikes. They are more biologically realistic than classic ANNs, and can potentially achieve a much better performance-power ratio. Recently, researchers from Zhejiang University and Hangzhou Dianzi University in Hangzhou, China successfully developed the Darwin Neural Processing Unit (NPU), a neuromorphic hardware co-processor based on spiking neural networks, fabricated by standard CMOS technology. With the rapid development of the "Internet of Things" and intelligent hardware systems, intelligent devices are pervasive in today's society, providing many services and conveniences to people's lives. But they also raise challenges of running complex intelligent algorithms on small devices.