A spectrogram of the sound of a human voice, used by voice-recognition software. The idea of artificial intelligence (AI) -- systems so advanced they can mimic or outperform human cognition -- first came to prominence in 1950, when British computer scientist Alan Turing proposed an'imitation game' to assess whether a computer could fool humans into thinking they were communicating with another human. Soon after, researchers at Princeton University in New Jersey built MADALINE, the first artificial neural network applied to a real-world problem. Their system, modelled on the brain and nervous system, learnt to solve a maze through trial-and-error. Since then, the rise of AI has been enabled by exponentially faster and more powerful computers and large, complex data sets.

Empowering algorithms to make potentially life-changing decisions about citizens still comes with significant risk of unfair discrimination, according to a new report published by the UK's Center for Data Ethics and Innovation (CDEI). In some sectors, the need to provide adequate resources to make sure that AI systems are unbiased is becoming particularly pressing – namely, the public sector, and specifically, policing. The CDEI spent two years investigating the use of algorithms in both the private and the public sector, and was faced with many different levels of maturity in dealing with the risks posed by algorithms. In the financial sector, for example, there seems to be much closer regulation of the use of data for decision-making, while local government is still in the early days of managing the issue. What is AI? Everything you need to know about Artificial Intelligence Although awareness of the threats that AI might pose is growing across all industries, the report found that there is no particular example of good practice when it comes to building responsible algorithms.

In Japan, not only can you have artificial intelligence pick your mate, but you can also have two giant Pikachu mascots standing by as you say I do. Finding the perfect mate can feel impossible, especially when in-person interactions have come to a screeching halt due to COVID-19 lockdowns. But if you live in Japan, the government there wants to help you find eternal love -- or at least your future spouse -- using artificial intelligence. In an effort to boost Japan's declining birth rate, the government has been trying to help single heterosexual men and women find true love so they get married and start families. The number of annual marriages in Japan has fallen from 800,000 in 2000 to 600,000 in 2019.

To gain an insight into these and other essential 2021 trends for businesses, Digital Journal caught up with Robert Prigge, CEO of Jumio. Addressing bias in AI algorithms will be a top priority causing guidelines to be rolled out for machine learning support of ethnicity for facial recognition. Prigge explains: "Enterprises are becoming increasingly concerned about demographic bias in AI algorithms (race, age, gender) and its effect on their brand and potential to raise legal issues. Evaluating how vendors address demographic bias will become a top priority when selecting identity proofing solutions in 2021." Prigge adds: "According to Gartner, more than 95 percent of RFPs for document-centric identity proofing (comparing a government-issued ID to a selfie) will contain clear requirements regarding minimizing demographic bias by 2022, an increase from fewer than 15 percent today. Organizations will increasingly need to have clear answers to organizations who want to know how a vendor's AI "black box" was built, where the data originated from and how representative the training data is to the broader population being served."

Books dedicated to Digital Transformation are on the rise in 2020. For that reason, we present a selection of the best Digital Transformation books recently written by talented authors. Every business that began before the Internet now faces the same challenge: How to transform to compete in a digital economy? Globally recognized digital expert David L. Rogers argues that digital transformation is not about updating your technology but about upgrading your strategic thinking. Based on Rogers's decade of research and teaching at Columbia Business School, and his consulting for businesses around the world, The Digital Transformation Playbook shows how pre-digital-era companies can reinvigorate their game plans and capture the new opportunities of the digital world. Rogers shows why traditional businesses need to rethink their underlying assumptions in five domains of strategy―customers, competition, data, innovation, and value. He reveals how to harness customer networks, platforms, big data, rapid experimentation, and disruptive business models―and how to integrate these into your existing business and organization. Rogers illustrates every strategy in this playbook with real-world case studies, from Google to GE, from Airbnb to the New York Times.

Causality knowledge is crucial for many artificial intelligence systems. Conventional textual-based causality knowledge acquisition methods typically require laborious and expensive human annotations. As a result, their scale is often limited. Moreover, as no context is provided during the annotation, the resulting causality knowledge records (e.g., ConceptNet) typically do not take the context into consideration. To explore a more scalable way of acquiring causality knowledge, in this paper, we jump out of the textual domain and investigate the possibility of learning contextual causality from the visual signal. Compared with pure text-based approaches, learning causality from the visual signal has the following advantages: (1) Causality knowledge belongs to the commonsense knowledge, which is rarely expressed in the text but rich in videos; (2) Most events in the video are naturally time-ordered, which provides a rich resource for us to mine causality knowledge from; (3) All the objects in the video can be used as context to study the contextual property of causal relations. In detail, we first propose a high-quality dataset Vis-Causal and then conduct experiments to demonstrate that with good language and visual representation models as well as enough training signals, it is possible to automatically discover meaningful causal knowledge from the videos. Further analysis also shows that the contextual property of causal relations indeed exists, taking which into consideration might be crucial if we want to use the causality knowledge in real applications, and the visual signal could serve as a good resource for learning such contextual causality.

Machine learning is the most relevant technology for analyzing space data for signs of intelligent life. At its core, machine learning is used to classify data at scale, find patterns, and generate insights that humans may not find.

Much of the tech industry's focus on the National Defense Authorization Act has revolved around President Trump's threat to veto the must-pass defense spending bill because it does not repeal Section 230. But the final version of the NDAA, passed by a vast majority of both chambers this week, contains a little-noticed provision that promises to reverberate across the industry: a pledge to increase government spending on artificial intelligence, quantum computing and 5G technology by $10 billion annually over the next five years. It's unclear exactly how much the government spends to bolster those technologies today, but it's likely closer to $1.5 billion. The Industries of the Future Act of 2020, which was supported by IBM and software industry trade group BSA, was introduced earlier this year amid a broader push from the White House -- and Ivanka Trump -- to invest more government resources in "industries of the future," meaning emerging technologies like quantum computing and artificial intelligence. It's part of a broader effort to funnel more resources toward ensuring the U.S. has a leg up on China in the so-called "race" to technological dominance.

Depending on how you look at ongoing AI news, artificial intelligence (AI) either promises unprecedented, sweeping changes in societies for good or threatens to supplant humanity in every imaginable way. We choose to focus on the first option, albeit with all the precautions about all the AI trends emerging from out of just about any lab furiously working to bring us the best artificial intelligence software. Thus, we present all the crucial artificial intelligence trends that you should know. If you're in business, these should give you ideas on how to navigate your own markets. If you're a casual observer, the list should tell you how AI should figure in your personal and social spheres in the near future. There is not any country that is not already touched by AI in any form.