What exactly is the Fourth Industrial Revolution -- and why should you care? The Fourth Industrial Revolution is a way of describing the blurring of boundaries between the physical, digital, and biological worlds. It's the collective force behind many products and services that are fast becoming indispensable to modern life. Think GPS systems that suggest the fastest route to a destination, voice-activated virtual assistants such as Apple's Siri, personalized Netflix recommendations, and Facebook's ability to recognize your face and tag you in a friend's photo. As a result of this perfect storm of technologies, the Fourth Industrial Revolution is paving the way for transformative changes in the way we live and radically disrupting almost every business sector.

Blockchain, hyped as the technology is at the moment, has not only quite some potential but has matured considerably over the past months. Still, after having critically looked at some promising use cases of blockchain throughout the last articles of this column, it is time move back to its original theme for a change. After all, the main theme of this column is Artificial Intelligence Trends and Tools. And bots, AI, and machine learning are exciting topics in their own right, let apart in their combination. Let's have a look at some chatbot and digital assistant developments … Since these are other technology areas that are also maturing fast.

Continuous representations have been widely adopted in recommender systems where a large number of entities are represented using embedding vectors. As the cardinality of the entities increases, the embedding components can easily contain millions of parameters and become the bottleneck in both storage and inference due to large memory consumption. This work focuses on post-training 4-bit quantization on the continuous embeddings. We propose row-wise uniform quantization with greedy search and codebook-based quantization that consistently outperforms state-of-the-art quantization approaches on reducing accuracy degradation. We deploy our uniform quantization technique on a production model in Facebook and demonstrate that it can reduce the model size to only 13.89% of the single-precision version while the model quality stays neutral.

Most literature in fairness has focused on improving fairness with respect to one single model or one single objective. However, real-world machine learning systems are usually composed of many different components. Unfortunately, recent research has shown that even if each component is "fair", the overall system can still be "unfair". In this paper, we focus on how well fairness composes over multiple components in real systems. We consider two recently proposed fairness metrics for rankings: exposure and pairwise ranking accuracy gap. We provide theory that demonstrates a set of conditions under which fairness of individual models does compose. We then present an analytical framework for both understanding whether a system's signals can achieve compositional fairness, and diagnosing which of these signals lowers the overall system's end-to-end fairness the most. Despite previously bleak theoretical results, on multiple data-sets -- including a large-scale real-world recommender system -- we find that the overall system's end-to-end fairness is largely achievable by improving fairness in individual components.

A group of researchers have published results from a shocking experiment that shows how voice controlled smart devices can be operated remotely using targeted laser beams to simulate human speech. The researchers announced Monday that they were able to control a Google Home and command it to remotely open the garage door from a separate building 230 feet away. Also susceptible were Amazon's Echo, Facebook Portal, a range of Android smartphones and tablets, and both iPhones and iPads. The experiments were conducted by a group of scientists from the University of Michigan and The University of Electro-Communications in Tokyo. 'It's possible to make microphones respond to light as if it were sound,' Takeshi Sugarawa, of University of Electro-Communications in Tokyo, told Wired.

The value of machine learning in finance is becoming more apparent by the day. As banks and other financial institutions strive to beef up security, streamline processes, and improve financial analysis, ML is becoming the technology of choice. Unlike so many hyped technologies and overrated buzzwords, machine learning is not going away -- probably ever. The ability of computer programs to learn on their own and improve over time creates new opportunities for industries across the board. While it is true that the naturally conservative financial industry was not at the front of the line for ML adoption, machine learning in fintech is now a common phrase.

Artificial Intelligence (AI) has become part of our everyday lives, from smart personal assistants such as Siri and Alexa, to security surveillance, and automatic parking systems. In education, particularly in higher education, Artificial Intelligence is transforming traditional methods of teaching for both students and teachers, making the whole learning experience more customised, efficient and engaging. The world has changed due to the momentum of technology, and teaching must reflect the fact that information is available at all times and in all places through smartphones and tablets. Today, students do not necessarily need to attend physical classes as long as they have computers and internet connection, and although challenging, AI has and will continue to benefit education in various ways. Firstly, from a Business School perspective, new technology can be used to enhance the efficiency of the school itself.

A few years back, the thought of a fully smart home might have seemed far-fetched. With everything around us becoming increasingly interconnected, home appliances and devices are now becoming ever more combined into the internet. The main goal of a smart home is to provide comfort, efficiency, and security to the homeowner. This trend is quickly shifting from a luxury to a necessity for a majority of homeowners, not only in the UK but worldwide. A smart home has a collection of interconnected home devices powered by intelligent applications.

A new report suggests consumers fully expect more automation and technology to be involved in their interactions with brands over the next ten years, but that presents a fine line between effectiveness and privacy for brands to walk. The report is based on a survey of 4,000 respondents across three dozen countries, conducted by Futurum Research on behalf of analytics company SAS. The respondents were a mix of consumers and marketing professionals and were asked about their anticipated use of technology in the year 2030. The main finding of the report is that there will be an increasing openness to automation when it comes to interactions between brands and consumers. According to SAS' analysis of the data, 67% of engagements done through digital channels in 2030 will be completed by smart systems, while 69% of business decisions made during that engagement will be completed by smart machines.

Apple's Siri Drew everyone's attention once it had been initial free in Oct 2011. Since then, quite a few virtual assistants have surfaced within the market, serving as instruments of automation that improve productivity. Similarly, Google currently and Amazon Alexa have even surpassed the expectation of users by Natural Language Processing(NLP) to form conversations additional interactive and fewer machine-like. From setting an easy reminder to narrating hour stories, these virtual assistants deliver varied capabilities by leverage computer science. Such Associate in the Nursing ecosystem -- on any platform -- can prove to be extraordinarily useful for entrepreneurs World Health Organization perpetually move with multiple knowledge sets and access data from totally different sources.