The release of DeepSeek's R1 – China's powerful new open-source AI model – has sent shockwaves through the global tech industry. Offered for free and royalty-free, it has disrupted financial markets, challenged the United States' dominance in artificial intelligence, and prompted fears that Silicon Valley's tightly guarded business model may no longer hold. DeepSeek's open-source launch is widely seen as a key trigger behind a trillion-dollar tech sell-off in the US, signalling deep investor anxiety over the commodification of AI and China's growing competitiveness. Dubbed "China's answer" to OpenAI's GPT‑4, R1 has unsettled investors and shifted global AI geopolitics. While full development expenses remain undisclosed, this points to a markedly more cost-effective model than proprietary counterparts.

Businesses in diverse industries have been leveraging AI in their digital marketing campaigns, right from optimizing their advertising efforts by targeting the most relevant audience, with the most relevant personalized messaging to increasing the engagement rate and marketing automation. Various digital marketing platforms and technologies powered by AI allow organizations to simplify and increase productivity for their marketing activities. Thus, corporate executives plan to boost the ROI in their business operations by harnessing artificial intelligence. An example of AI in digital marketing is advertising on platforms like Google and Facebook that leverage AI to help advertisers reach their potential target audience based on the behavior of their users on the platform, or different bid strategies like target CPA, target ROAS, maximize conversions powered by AI to control the delivery of ads to users that are more likely to convert and meet the advertisers' targets and boosts ROI. Businesses are creating an AI-driven marketing automation approach and leveraging existing tools powered by AI to generate better ROI.

Natural language processing (NLP) is one of the hottest fields in artificial intelligence (AI) and machine learning (ML) right now. The global NLP industry is expected to reach US$42.04 billion by 2026, with a CAGR of 21.5%, according to Mordor Intelligence. This rapid expansion of NLP has resulted in the emergence of new trends and advancements in the field. Let's take a look at some NLP trends to look out for in 2022. Transfer learning is a machine learning approach that involves training a model for one job and then repurposing it for a related activity.

"Open source software quietly affects nearly every issue in AI policy," wrote Alex Engler in a Brookings Institution briefing, yet this is barely discussed by government policymakers. This is a mistake, and it's one that crosses the political aisle. The Trump administration barely mentioned open source in its AI policies, while the Obama administration touted open source as driving AI innovation but stopped there. In Europe things are no better, with new regulations about AI skipping the topic of open source entirely. Given how prevalent open source has become in the artificial intelligence software that companies and governments use, policymakers would do well to pay attention, noted Engler.

Open-source software quietly affects nearly every issue in AI policy, but it is largely absent from discussions around AI policy--policymakers need to more actively consider OSS's role in AI. Open-source software (OSS), software that is free to access, use, and change without restrictions, plays a central role in the development and use of artificial intelligence (AI). Across open-source programming languages such as Python, R, C, Java, Scala, Javascript, Julia, and others, there are thousands of implementations of machine learning algorithms. OSS frameworks for machine learning, including tidymodels in R and Scikit-learn in Python, have helped consolidate many diverse algorithms into a consistent machine learning process and enabled far easier use for the everyday data scientist. There are also OSS tools specific to the especially important subfield of deep learning, which is dominated by Google's Tensorflow and Facebook's PyTorch.

"The big tech is banking heavily on AI, Cloud and 5G technologies to retain customers and drive growth" A global emergency can smother your business, government lawsuits can break your company, competitors with trillion-dollar market value can wipe your organisation off the map. But what would happen when all three come together in the same year? The pandemic brought the world to a standstill. The internet giants, however, came out of it unscathed. Apple, Amazon, Google and Facebook, popularly known as the big four, have not only survived a combination of calamities but registered profits and left the Wall Street analysts dumbfounded.

Google's testimony to an Australian Senate committee on Friday threatening to withdraw its search services from Australia is chilling to anyone who cares about democracy. It marks the latest escalation in the globally significant effort to regulate the way the big tech platforms use news content to drive their advertising businesses and the catastrophic impact on the news media across the world. The news bargaining code, which would require Google and Facebook to negotiate a fair price for the use of news content, is the product of an 18-month process driven by the competition regulator. That legislation is currently before the Australian parliament, where a Senate committee is taking final submissions from interested parties. The Google bombshell makes explicit what has been a slowly escalating threat that a binding code would not be tenable.

Three years into his stint teaching machine learning at MIT Sloan, finance lecturerMichael Shulman has just one complaint: It's hard to keep up. "It's such a fast-moving field, a lot of what's state-of-the-art now wasn't invented when I taught the course a year ago," he said. Officially titled Advanced Data Analytics and Machine Learning in Finance, the course reflects a move in finance, normally a tech-cautious industry, to embrace machine learning to help make faster, better-informed decisions. Specifically, financial analytics firms are turning to natural language processing to parse textual data hundreds of thousands of times faster and more accurately than humans can, said Shulman, head of machine learning at Kensho. A casual observer might assume financial data to be more numerical than textual, but Shulman said that's not the case.

Data is turning out to be more valuable than we thought. Google and Facebook's ad revenues exceeded $200 billion last year. They can hope to have a bigger source of income soon, thanks to the income generated by the Artificial Intelligence (AI) business built using the data of billions of individuals. No wonder, getting hold of data by paying top dollars is the new game in the digital world. This may explain the sudden investments of Google, Facebook, Intel, and many others in India, one of the largest data generators of the world.

Can artificial intelligence ever boost productivity of firms and industries the way the PC and ... [ ] networking did in the '80s and '90s? A big pastime of economists in the 1980s and 1990s was trying to gauge how much corporate and industrial productivity would benefit from the then-novel phenomena of personal computers, workgroup servers, and computer networking. At first it was hard to see, but in time, economists did indeed find evidence that information technology contributed to boosting economic productivity. It's too soon to expect to see data showing a similar boom from artificial intelligence, today's big IT revolution. The technology is just becoming industrialized, and many companies have yet to even try to use things such as machine learning in any significant way.