SoftBank Group's investments in AI will be financed through a loan in which Mizuho Bank, Sumitomo Mitsui Banking and JPMorgan Chase serve as lead underwriters -- a sign of the Japanese tech investor's ability to secure financing for its outsized ambitions. The one-year 15 billion bridge loan, one of the biggest borrowings SoftBank has pulled off to date, will be financed by 21 banks and includes 1.35 billion from Mizuho, 1.25 billion from SMBC and 1 billion from JPMorgan, according to people familiar with the matter. It also includes a combined 950 million from HSBC Holdings and Barclays and 850 million jointly from seven banks including Goldman Sachs Group Inc., MUFG Bank and Credit Agricole, said the people, who asked not to be named as the details of the financing remain private. Representatives of the banks also declined to comment.

In a note released Friday JPMorgan analyst Doug Anmuth wrote that AI is reaching "an inflection point" at Alphabet. "Amidst a challenging operating environment, we're encouraged by upcoming AI products & what feels like deeper commitment to rationalize the cost structure, though there is still some uncertainty around magnitude and timing," he wrote. See Now: Google suffered'pullback' in ad spending over holidays, Alphabet stock falls after earnings The analyst explained that AI has been integral to Alphabet's business for many years, driving search and broader ads performance, while also supporting its cloud business. "After years of heavy investments along with recent competitive pressures, Google will roll out several new AI tools over the next few months," he added, highlighting the company's efforts to layer its LaMDA chatbot conversation technology across the company's suite of products, specifically in search and in a possible standalone product. In addition to the company's efforts to deliver AI tools to app developers, Anmuth also pointed to its plans to introduce AI-powered features to Workspace and "large language models" to Gmail and Docs.

The road to maturity for any technology in the enterprise is long and arduous. Take data and analytics platforms as an example. Data from 451 Research's Voice of the Enterprise series in March found a third of companies surveyed were still yet to fully embrace a data-driven approach to strategic decision making. If that is the case, whither artificial intelligence? Writing for Enterprise Talk earlier this month, Swapnil Mishra notes of businesses still being in the'AI adolescence' phase, citing research from Accenture which found 63% of 1,200 companies polled were still experimenting with projects.

Electric vehicles are no longer the main way to compare carmakers, with self-driving cars now the main race over the coming years, according to JPMorgan. The investment bank reckons Daimler (ETR:DAI), Stellantis NV (NYSE:STLA, EPA:STLA), Renault and Volkswagen Group (XETRA:VOW) "will benefit the most from this trend" and they are its key picks among the traditional automotive original equipment manufacturers (OEMs), a group that also includes BMW, Ford Motor Company (NYSE:F), General Motors Company (NYSE:GM), Toyota, Mazda, Honda, Hyundai and Kia. "Electrification is no longer a differentiating factor amongst OEMs, in our view," the US investment bank said. "Rather, cash generation and the race into autonomous driving will be over the coming years," JPM said in a note to clients on Monday. Looking at the auto market in the past year, the analysts said it was becoming "increasingly evident" that the traditional OEMs are using their strong free cash flow to "speed their way into electrification and autonomous driving to compete against new start-ups".

We suggested in January that it might be a good idea to familiarize yourself with quantum computing if you want to maximize your future employability in financial services. A new academic paper from JPMorgan's Future Lab for Applied Research and Engineering helps explain why. Authored by Marco Pistoia, JPMorgan's head of quantum technology and head of research, plus members of his team, the paper stresses that quantum computing will impact financial services sooner than you think. Goldman Sachs and JPMorgan have both been building teams of quantum researchers and Goldman has already used quantum methods to speed up derivatives pricing by over a thousand times. The finance industry stands to benefit from quantum computing "even in the short term," says JPMorgan.

This week's top reads in banking, fintech, payments, cybersecurity, AI, IoT, risk management and much more In this weeks selection; Top Reads At Fault or Default?

Several American banks have started using surveillance software and computer vision to watch people using their services. Computer vision is a part of artificial intelligence that uses computers to understand the world we see. A Reuters news agency investigation found that the software is used to learn about customers, watch employees and spot people sleeping near Automatic Teller Machines (ATMs). Banks like the City National Bank of Florida and JPMorgan Chase & Co have tested facial recognition and artificial intelligence (AI) technologies. The growth of AI tools within the banking industry could signal the spread of the technology into other industries.

JPMorgan Chase sees artificial intelligence (AI) as critical to its future success. And the mega-bank has a big advantage over many of its smaller rivals: the massive amount of data it gathers from sources such as the 50% of U.S. households with which it has some form of relationship and the $6 trillion worth of payment flows it handles daily. But until recently, identifying and pulling in relevant data to train AI models was taking up around 60% of the time of the bank's growing army of data scientists. That was an inefficient use of an expensive and relatively scarce resource. Now a new data platform the bank has developed, called OmniAI, is helping it to get relevant data into its models much faster.

JPMorgan Chase sees artificial intelligence (AI) as critical to its future success. And the mega-bank has a big advantage over many of its smaller rivals: the massive amount of data it gathers from sources such as the 50% of U.S. households with which it has some form of relationship and the $6 trillion worth of payment flows it handles daily. But until recently, identifying and pulling in relevant data to train AI models was taking up around 60% of the time of the bank's growing army of data scientists. That was an inefficient use of an expensive and relatively scarce resource. Now a new data platform the bank has developed, called OmniAI, is helping it to get relevant data into its models much faster.

Bank of America hits a milestone with Erica. It's not only fintechs that can lure millions of customers their way. Bank of America's voice-activated virtual assistant Erica now has more than ten million users. It's also on track to complete 100 million client requests in the coming weeks and that's all since its launch in June of 2018. "Erica is ushering in a new era of personalized banking and providing our clients never-before-possible convenience," said David Tyrie, head of advanced solutions and digital banking at Bank of America in prepared remarks.