The mantra of modern technology is to improve and innovate continuously. It makes sense as we strive to look for more improved ways to get processes, actions and activities done. Automation and machine learning, for instance, is currently used across many industries to streamline basic processes and remove the repetition from a normal worker's routine. Not to mention, machines tend to be more efficient and less resource intensive. A robotic or automated system continues to work at its set performance, never tiring, growing hungry or getting burnt out.
Blockchain technology has been the new kid on the "block" for some time now and is especially hot when enabling new currencies with the rollercoaster rides that these have recently taken. However, blockchains are not limited to crypto-currencies but are widely being adopted by other industries as well, especially the automotive industry. According to Frost and Sullivan, 10–15% of connected vehicle transactions are expected to be on blockchain by 2025. Toyota is exploring blockchain technology in collaboration with MIT Media Lab to develop a new mobility ecosystem that could accelerate the development of autonomous driving technology, particularly with secure data sharing, car/ride share transactions and usage-based insurance. German supplier ZF and IBM announced in 2017 that they were jointly developing Car eWallet, a payment technology targeting future mobility services.
Most people are not very familiar with the concept of artificial intelligence (AI). As an illustration, when 1,500 senior business leaders in the United States in 2017 were asked about AI, only 17 percent said they were familiar with it.1 A number of them were not sure what it was or how it would affect their particular companies. They understood there was considerable potential for altering business processes, but were not clear how AI could be deployed within their own organizations. Despite its widespread lack of familiarity, AI is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decisionmaking. Our hope through this comprehensive overview is to explain AI to an audience of policymakers, opinion leaders, and interested observers, and demonstrate how AI already is altering the world and raising important questions for society, the economy, and governance. In this paper, we discuss novel applications in finance, national security, health care, criminal justice, transportation, and smart cities, and address issues such as data access problems, algorithmic bias, AI ethics and transparency, and legal liability for AI decisions. We contrast the regulatory approaches of the U.S. and European Union, and close by making a number of recommendations for getting the most out of AI while still protecting important human values.2 Although there is no uniformly agreed upon definition, AI generally is thought to refer to "machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention."3 According to researchers Shubhendu and Vijay, these software systems "make decisions which normally require [a] human level of expertise" and help people anticipate problems or deal with issues as they come up.4 As such, they operate in an intentional, intelligent, and adaptive manner. Artificial intelligence algorithms are designed to make decisions, often using real-time data. They are unlike passive machines that are capable only of mechanical or predetermined responses. Using sensors, digital data, or remote inputs, they combine information from a variety of different sources, analyze the material instantly, and act on the insights derived from those data. With massive improvements in storage systems, processing speeds, and analytic techniques, they are capable of tremendous sophistication in analysis and decisionmaking.
Machine learning is a subset of AI systems that has become an essential tool for many. The phrase is used to describe the process of a programs ability to learn without being explicitly programmed. Essentially, we are talking about programs that write themselves. Investment firms, traders and hedge funds are using machine learning to create artificial intelligence with the ability to predict when the market will rise and fall. After making predictions these programs then automatically buy and sell based on those predictions.
Look at your boss and remember his or her face. No matter your relationship with them, pretty soon your boss will be faceless, ruthless and will be looking to fire you at any moment's notice. Artificial Intelligence is garnering praise for its ability to create efficiency, save time and labor and generate income, as is the case for the UAE. Great, but that means you could be out of a job you love. Also, trusting this technology too much could also put your life at risk.
Russia's top court has ruled the Telegram app, which offers encrypted messaging services, can be forced to provide user data to authorities. The Supreme Court threw out an appeal by Telegram protesting against demands from the Federal Security Service intelligence agency (FSB) for it to hand over data from its users. People behind the app, which has caused controversy for allegedly being favoured by extremists, argued the FSB violated consumer rights by demanding encryption keys and chat histories. Telegram has been given 15 days to comply by Russia's communications regulator, or it risks being blocked in the country. Ramil Akhmetgaliev, the lawyer for the messaging company, was quoted by Russian news agencies as saying Telegram considers it essential to "keep users' communications secret".
Though its time horizon can't be predicted, artificial intelligence (AI) promises to foundationally influence modern society, for better or worse. A sub-genre of AI -- machine learning -- has garnered particular attention from the pundits for its potential impact on the world's most important industries.
We have spoken about machine learning and the internet of things as tools to optimize location analytics in logistics and supply chain management. It's an accepted fact that technology, especially cloud-based, can benefit companies by optimizing routes and predicting the accurate estimated time of arrivals (ETAs). The direct business value of this optimization lies in the streamlining of various fixed and variable costs associated with logistics.