Panic about new technologies is nothing new, and artificial intelligence is no exception. This week more than 1,800 people have signed an open letter calling for at least a six-month pause on training AI systems that are'more powerful than GPT-4′ – the latest chatbot released by Open AI. The signatories – who include the likes of Elon Musk, Andrew Yang and Steve Wozniak – want governments to impose a moratorium if AI labs don't stop their research voluntarily. Meanwhile here in the UK, the Government recently released its own AI regulation strategy. The letter cites a number of concerns about AI: 1) disseminating dis/misinformation 2) ushering in a period of widespread unemployment, and 3) the creation of nefarious robot overlords.

Elon Musk and a group of artificial intelligence experts and industry executives are calling for a six-month pause in training systems more powerful than OpenAI's newly launched model GPT-4, they said in an open letter, citing potential risks to society and humanity. The letter, issued by the non-profit Future of Life Institute and signed by more than 1,000 people including Musk, Stability AI CEO Emad Mostaque, researchers at Alphabet-owned DeepMind, as well as AI heavyweights Yoshua Bengio and Stuart Russell, called for a pause on advanced AI development until shared safety protocols for such designs were developed, implemented and audited by independent experts. "Powerful AI systems should be developed only once we are confident that their effects will be positive and their risks will be manageable," the letter said. The letter also detailed potential risks to society and civilization by human-competitive AI systems in the form of economic and political disruptions, and called on developers to work with policymakers on governance and regulatory authorities. The letter comes as EU police force Europol on Monday joined a chorus of ethical and legal concerns over advanced AI like ChatGPT, warning about the potential misuse of the system in phishing attempts, disinformation and cybercrime.

Although artificial intelligence (AI) is getting more advanced due to an exponential rate of development, limitations to this modern technology still exist. So, can synthetic data be the solution for all AI-related concerns? In the fourth industrial revolution, every industry sector has discovered the potential of modern technologies; such as artificial intelligence (AI) and machine learning (ML). Almost every other organization is deploying AI to create more efficient business processes and to ensure better customer satisfaction. But, startups, SOHOs, and small and medium businesses (SMBs) face a major issue while adopting AI- it's called the cold start problem.

Although artificial intelligence (A)I is getting more advanced due to an exponential rate of development, limitations to this modern technology still exist. So, can synthetic data be the solution for all AI-related concerns? In the fourth industrial revolution, every industry sector has discovered the potential of modern technologies; such as AI and ML. Almost every other organization is deploying AI to create more efficient business processes and to ensure better customer satisfaction. But, startups, SOHOs, and small and medium businesses (SMBs) face a major issue while adopting AI- it's called the cold start problem.

Artificial intelligence (AI) holds real potential for improving both the speed and accuracy of medical diagnostics. But before clinicians can harness the power of AI to identify conditions in images such as X-rays, they have to'teach' the algorithms what to look for. Identifying rare pathologies in medical images has presented a persistent challenge for researchers, because of the scarcity of images that can be used to train AI systems in a supervised learning setting. Professor Shahrokh Valaee and his team have designed a new approach: using machine learning to create computer generated X-rays to augment AI training sets. "In a sense, we are using machine learning to do machine learning," says Valaee, a professor in The Edward S. Rogers Sr. "We are creating simulated X-rays that reflect certain rare conditions so that we can combine them with real X-rays to have a sufficiently large database to train the neural networks to identify these conditions in other X-rays."

Lee Kai-Fu has always been very bullish about the future of artificial intelligence (AI) in China. He started off his keynote speech at an AI conference at the Massachusetts Institute of Technology in November by predicting that self-driving cars will become a mass phenomenon in the U.S. in 15 to 20 years. But in China, he said, it will take "more like 10 years." "Although there are concerns about whether there is an emerging AI bubble in China, I'd say there isn't one," he told Caixin. Lee is a real insider when it comes to assessing the state of AI development in both North America and China. He completed his doctorate in computer-aided speech recognition at Carnegie-Mellon University (CMU) in 1988 and went on to work at Apple Inc., Silicon Graphics Inc. and Microsoft Corp., and head Google Inc.'s China business.