When it comes to technology, the World Economic Forum is all about staying ahead of the curve. So it's no surprise that the organization is closely monitoring the rise of artificial intelligence (AI). In a recent report, the Forum's Council on the Future of Artificial Intelligence and Robotics examines the implications of AI for business and society. The report includes a number of key findings, including the need for businesses to embrace AI and the potential for AI to create new jobs. The report also highlights the importance of ethical considerations when it comes to AI. As the Forum notes, "AI technology is only as good as the values it reflects and the choices it makes." Overall, the report paints a positive picture of the future of AI. As the Forum notes, "AI has the potential to unlock new frontiers of human potential and economic growth."

The graph represents a network of 1,311 Twitter users whose tweets in the requested range contained "tech4good", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Saturday, 13 August 2022 at 18:33 UTC. The requested start date was Saturday, 13 August 2022 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 13-day, 19-hour, 26-minute period from Saturday, 30 July 2022 at 04:34 UTC to Saturday, 13 August 2022 at 00:00 UTC.

About a decade ago, deep-learning models started achieving superhuman results on all sorts of tasks, from beating world-champion board game players to outperforming doctors at diagnosing breast cancer. These powerful deep-learning models are usually based on artificial neural networks, which were first proposed in the 1940s and have become a popular type of machine learning. A computer learns to process data using layers of interconnected nodes, or neurons, that mimic the human brain. As the field of machine learning has grown, artificial neural networks have grown along with it. Deep-learning models are now often composed of millions or billions of interconnected nodes in many layers that are trained to perform detection or classification tasks using vast amounts of data.

Artificial intelligence (AI) is usually associated with getting us to the future faster, but it can also be a powerful tool in uncovering the past. Here are 6 ways the technology is being used around the world to help us understand the past and prepare for the future. An inscription showing Algorithm helping historians restore Greek inscriptions. An AI algorithm called Ithaca is helping historians restore ancient Greek inscriptions. Researchers at British AI firm DeepMind trained the algorithm on around 60,000 ancient Greek texts from across the Mediterranean that were written between 700 BC and AD 500.

AI-enabled smart toys come in an exponentially growing diversity and inhibit the most known social environments of children. The Market Research Future group, for instance, projects that the global market share of such toys will grow by 26% and reach 107.02 billion USD by 2030. Smart toys, as highlighted by the Generation AI Initiative of the World Economic Forum, can exhibit highly positive effects on children's development when designed responsibly. Complaints received by the Federal Trade Commission (FTC) as well as an investigation of the Norwegian Consumer Organisation and rich scholarly research however also point to the severe impacts that smart toys can have on children's development. The non-transparent ways in which some smart toys exchange data for algorithmic analysis with other AI-enabled devices, e.g., through Bluetooth connection, demonstrate how weak cyber security features can violate children's privacy and jeopardize safety.

Over the last few years, responsible AI has gone from a niche concept to a constant headline. Responsible, trustworthy AI is the subject of several documentaries, books, and conferences. The more we make responsible AI an expectation and a known commodity, the more likely we are to make it our reality. This enables us to flourish with more accessible AI. This is our shared goal through the Responsible AI Badge certification programme for senior executives.

Imagine you want your new smart thermostat to quickly turn up the heat so that your house will be warm after your get home from work on an unusually cold day. You connect from your smartphone and ask it to act. You won't know it, but that action may take several seconds as it moves your request to the cloud and receives instructions back. Now imagine the self-driving car you're in suddenly senses a dog running into the road in front of you. The car needs to react in milliseconds to avoid a disaster.

Technology and Artificial Intelligence (AI) have brought in mixed reactions concerning job security and HR as a profession has not been spared. A recent report by the World Economic Forum (WEF) however gives a picture that technology and Artificial Intelligence (AI) will make HR more efficient and effective.1 At the Digital HR Innovation Summit back in 2019 in Amsterdam, we learned that at that time only 6% of companies were actively using AI in HR globally.2 Most of the organizations were at an exploratory stage where they were learning whether AI works for them or not. PWC's recent report confirms, the pandemic has drastically accelerated the digital transformation agenda through the integration of disruptive technologies.

Can it help in judicial processes to reduce the pendency of cases? The spread of smart technologies like AI across organizations is being driven by the seeking of competitive advantage, but are senior leaders prepared for the impact? The green energy revolution promised by nuclear fusion is now a step closer, thanks to the first successful use of a cutting-edge artificial intelligence system to shape the superheated hydrogen plasmas inside a fusion reactor. How can AI support diversity, equity and inclusion? There is a history of distrust between communities of color and emerging technologies such as AI, particularly in the justice system.

Deep learning, at its essence, learns from examples -- the way the human brain does. It's imitating the way humans acquire certain types of knowledge. Because deep learning processes information in a similar manner, it can be used to do things people can do – for example, learning how to drive a car or identifying a dog in a picture. Deep learning is also used to automate predictive analytics – for example, identifying trends and customer buying patterns so a company can gain more customers and keep more of them. You know those sections on retail sites that show items "frequently bought together" when you're purchasing a new screwdriver?