If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Gartner Supply Chain Executive Summit -- IBM (NYSE: IBM) today launched Business Transactional Intelligence (BTI), an AI-powered solution that offers anomaly detection and visualization capabilities for mitigating supply chain disruptions and accelerating data-driven decision making. BTI, part of IBM's Supply Chain Business Network, enables companies to garner deeper insights into supply chain data to help them better manage, for example, order-to-cash and purchase-to-pay interactions. The technology does this, in part, using machine learning to identify volume, velocity and value-pattern anomalies in supply chain documents and transactions. Machine learning is a method used to teach artificial intelligence how to learn from data, spot patterns and make decisions on its own. This enables companies to discover potential issues faster and resolve them before they escalate and impact the business.
According to recent and separate studies from Gartner, Harvey Nash/KPMG, and O'Reilly, somewhere between 24% and 37% of organizations are at least moderately investing in machine learning and artificial intelligence. Much of this will be AI/ML embedded in applications like chatbots, recommendation engines, and virtual assistants and some consider RPAs a form of AI/ML. But it also means that more organizations are testing AI/ML on their proprietary data, developing models, and connecting models to their end user applications. The most advanced organizations using AI/ML like Twitter and Facebook are developing entire model development lifecycles to support ongoing model improvements are retraining. Integrating Applications with Machine Learning Models As a full stack developer or a solutions architect, it's quite likely that you'll be asked to integrate applications and data pipelines to ML models.
Chatbots are noticeably one of the most popular AI technologies across the world. From simplifying business workflows, enhancing employee and customer experience to reducing costs, chatbots provide numerous benefits for organizations of all sizes. The use cases of chatbots are diverse and vary across departments and industries. Most enterprise leaders are yet to understand and explore the full potential of chatbots.
San Diego-based startup TuSimple said its self-driving trucks will begin hauling mail between USPS facilities in Phoenix and Dallas to see how the nascent technology might improve delivery times and costs. A safety driver will sit behind the wheel to intervene if necessary and an engineer will ride in the passenger seat. If successful, it would mark an achievement for the autonomous driving industry and a possible solution to the driver shortage and regulatory constraints faced by freight haulers across the country. The pilot program involves five round trips, each totaling more than 2,100 miles (3,380 km) or around 45 hours of driving. It is unclear whether self-driving mail delivery will continue after the two-week pilot.
It's always tempting to begin an article on AI with some form of science fiction analogy, but the truth is that the technology has been around almost as long as the genre! Some of the people reading this, like me, may remember the burst of excitement around AI in the 1980s and 90s. We've come a long way since then, and many facets of AI, especially machine learning, have become very mature. The increasing digital transformation happening within manufacturing is bringing the potential of AI into focus. International Data Corp., a technology research firm based in Framingham, MA, suggests that manufacturing companies are "at the heart of a perfect storm, both living with and seeking to exploit disruptive technologies such as cloud, big data, AI-assisted analytics and the Internet of Things (IoT), while facing increasing IT security challenges, regulatory pressures and a changing workforce".1 The explosion of big data and IoT is pivotal.
The behavioral revolution in economics was triggered by a simple, haunting question: what if people don't act rationally? In the online world, once expected to be a place of ready information and easy collaboration, lies and hate can spread faster than truth and kindness. For example, when predicting sales, employees often hide bad deals and selectively report the good ones. AI stands at the crossroads of the behavioral question, with the potential to make matters worse or to elicit better outcomes from us. The key to better outcomes is to boost AI's emotional quotient -- its EQ.
"You want to ride the wave rather than getting slammed by its disruption. You don't want to be Blockbuster Video or Sears, you want to be Netflix or Amazon." That was how Dave Bluey, assistant professor of practice and career advisor with the Department of Management, explained the reasoning behind the Department of Management's symposium, "How Artificial Intelligence Will Impact Your Career." Over 250 hundred students gathered for a panel discussion led by industry experts to hear about – and in some cases see – the impact artificial intelligence may have on their future careers. The event was a partnership between the Management Department and leading firms in the areas of machine learning, artificial intelligence, and robotics in an on-going Digital Transformation Series at Virginia Tech.
Spinning up dark matter simulations is computationally expensive so a team of cosmologists are turning to AI models instead. Generative adversarial networks or GANs are good at learning patterns from data and reproducing them in new samples. In this case, the team led by researchers from the Lawrence Berkeley National Laboratory used weak gravitational lensing maps as input to simulate more of the same images as output. They named the model CosmoGAN and have published a paper in Computational Astrophysics and Cosmology earlier this month. Gravitational lensing provides opportunities for scientists to study the effects of dark matter in the universe.
The funds raised in this round are to be used for recruiting more high-caliber talents and developing new technologies. Xiaoduo AI has been engaged in the AI customer service application scenario for many years, which is a highlighted project in Chengdu's AI industry. From the perspective of the current enterprise volume and technical background, it has the potential to become a hidden champion in the field of AI customer service. Founded in 2014, Xiaoduo AI adheres to the vision of "becoming the AI expert of enterprises and creating better communication and service with AI", and has been committed to improving the efficiency of the customer service industry by leveraging the AI technology. At present, Xiaoduo AI serves more than 20,000 governmental and enterprise clients, including Meituan, Zhuanzhuan, YouShop, EMS, Robam Electric Appliance, XGIMI, TmallGenie, 1919.cn,