Tencent is one of China's largest technology companies Chinese tech giant Tencent doesn't seem to be affected by US export bans of computer chips that are crucial to the development of artificial intelligence systems – but even if such bans were more stringent, they may not be able to slow the country's AI advancement, US research shows. Ritwik Gupta and his colleagues at the University of California, Berkeley, have analysed publications released by researchers at Tencent about the firm's latest models, including its Hunyuan AI models. The team's findings suggest that, in recent months, Tencent has…

One of today's primary priorities of companies is to improve the Customer Experience (CX) to increase customer satisfaction and reduce churn. However, "just 2 percent of organizations reached the top stage of CX maturity [and] most organizations are in early stages of CX maturity" (Dorsey et al., 2022). According to a recent study by Qualtrics (2022), 47 percent of customers ranked support as the second most important area of improvement in CX. One major factor of customer satisfaction identified in recent research (e.g., Service Excellence Research Group, 2021) is the speed at which customer support answers customer inquiries. Demand for customer support is rising and often exceeds the supply of available support agents. Especially missing knowledge and multiple re-routings between support agents are major factors for delays in resolution time. Further research suggests that due to information overload, the quality of decisions decreases with the number of decisions (Hemp, 2009; Viegas et al., 2015). In most recent studies, lack of time and resources are mentioned as the main issues in customer support, which harm the performance and, ultimately, the customer experience (HubSpot, 2022; Serrano et al., 2021).

AI is used for a variety of purposes in the fintech industry. Often mixed with machine learning, a method of training AI, AI in fintech involves "intelligent" systems that automate or enable solutions for complex problems and processes, and/or uncover insights in data. Applications include Anti-Money Laundering (AML) processes, fraud checks, credit checks, decision support, risk assessments, and more. With great technology comes great responsibility and the application of AI and data collection in financial services is one that raises many questions in terms of management, security, and regulation. The European Union recently introduced rules that will begin to shape the way AI is used, with a particular focus on the financial services sector.

The machine learning model analyzed health data from nearly 700,000 patients in Alberta who received opioid prescriptions between 2014 and 2018, cross-referencing 62 factors such as the number of doctor and emergency room visits, diagnoses, and sociodemographic information. Researchers found the top risk factors for opioid use disorder included frequency of opioid use, high dosage, and a history of other substance use disorders. The model predicted high-risk patients with an accuracy of 86 per cent when it was validated against a new sample of 316,000 patients from 2019. According to the study, the findings suggest early detection of opioid use disorder is possible with a data-driven approach and may provide timely clinical intervention and policy changes to help curb the current crisis. "It's important that the model's prediction of whether someone will develop opioid use disorder is interpreted as a risk instead of a label," said first author Yang Liu, a post-doctoral fellow in psychiatry, in the release.

The pace at which AI is being embraced across organizations is fascinating. From automating basic tasks to creating something unique, AI has come a long way in making lives easier, processes efficient, and even fulfilling the skill gap. We will discuss what are the top 4 AI trends that will dominate 2023 and how businesses can make the best use of them. Democratization of AI The democratization of AI makes this technology accessible to everyone, not just tech-savvy professionals. This means there's little to no barrier to entry when it comes to embracing AI in everyday business processes. With simple drag-and-drop features, anyone with a vision can bring it to life by creating, testing, and deploying the app.

Urgent action is needed to tackle an artificial intelligence (AI) skills crisis that is already stifling US productivity and innovation, new research has found. Published by analytics leader SAS, How to Solve the Data Science Skills Shortage is a report based on a survey of decision makers from major US firms spanning nine sectors, including banking, insurance, government and retail. Fortune Business Insights projects the global artificial intelligence market to grow from $387 billion in 2022 to nearly $1.4 trillion by 2029.[1] Correspondingly, AI and machine learning are top investment priorities over the next one to two years, according to 43% of SAS survey respondents. That is well ahead of data technology stalwarts such as data visualization (25%), data analytics (22%) and big data (17%).

Artificial intelligence has moved from the realm of science fiction into our pockets. And while we are nowhere close to engaging with AI as sophisticated as the character Data from Star Trek, the forms of artificial narrow intelligence that we do have inform hundreds of everyday decisions, often as subtle as what products you see when you open a shopping app or the order that content appears on your social media feed. Examples abound of the real and potential benefits of AI, like health tech that remotely analyses patients' vital signs to alert medical staff in the event of an emergency, or initiatives to identify vulnerable people eligible for direct cash transfers. But the promises and the success stories are all we see. And though there is a growing global awareness that AI can also be used in ways that are biased, discriminatory, and unaccountable, we know very little about how AI is used to make decisions about us. The use of AI to profile people based on their personal information – essentially, for businesses or government agencies to subtly analyse us to predict our potential as consumers, citizens, or credit risks – is a central feature of surveillance capitalism, and yet mostly shrouded in secrecy.

A group of researchers have come to the terrifying conclusion that containing super-intelligence AI may not be possible. They claim that controlling the AI would fall beyond human comprehension. According to the Journal of Artificial Intelligence Research, in the paper titled, 'Superintelligence Cannot be Contained: Lessons from Computability Theory', researchers have argued that total containment (in principle) would be impossible due to fundamental limits inherent to computing. It further claims that it is mathematically impossible for humans to calculate an AI's plans, thereby making it uncontainable. The authors cite that implementing a rule for artificial intelligence to "cause no harm to humans" would not be an option if humans cannot predict the scenarios that an AI may come up with.

As AI transforms global workplaces, new research shows that data literacy will be the most in-demand skill by 2030. According to research from Qlik, a little over one in five employees believe their employer is preparing them for a more data-oriented and automated workplace (21%). This is despite most business leaders predicting an upheaval in working practices due to the rapid onset of AI. The report, Data Literacy: The Upskilling Evolution, found that 35% of employees say they had changed jobs in the last 12 months because their employer wasn't offering enough upskilling and training opportunities. Developed by Qlik in partnership with The Future Labs, the report combines insights from expert interviews with surveys from over 1,200 global C-level executives and 6,000 employees.

Neural networks are one of the most exciting developments in data science. These advanced AI models could revolutionize forecasting and fraud detection processes, and likely will before long. That makes the recent discovery that malware can hide within them all the more concerning. A group of researchers recently revealed that it's possible to hide at least 36.9 Despite the considerable size of this malicious data, the network showed only a 1% drop in accuracy, helping it slip by unnoticed.