Generative artificial intelligence tends to grow in leaps and bounds.
And increasingly, so do the companies behind the technology, including Microsoft, which reached a market valuation of $3 trillion Wednesday (Jan. 24) due to the impact of AI on investor’s appetites, consolidating its position as one of the largest public stocks.
While the addition of ChatGPT has reportedly not helped Microsoft’s Bing search product take on Google’s flagship 800-pound Gorilla, it still puts the Redmond, Washington-based tech giant in rarified company. Apple is the only other public business to have crossed the $3 trillion threshold.
Still, all that growth hasn’t come without scrutiny.
The Federal Trade Commission (FTC) opened an inquiry into ongoing investments and partnerships in the AI sector Thursday (Jan. 25), ordering Big Tech companies Alphabet, Amazon, Anthropic, Microsoft and OpenAI to turn over information about their ecosystem investments and partnerships as it investigates any effect they may be having on AI’s competitive landscape.
Here is the weekly roundup of the can’t-miss AI news that PYMNTS found, tracked and covered.
Read also: AI’s Role in Advancing Real-Time Payments
Platforms, providers and businesses are embedding AI into end-user touchpoints.
PayPal announced Thursday that it is introducing what it termed a “reimagined checkout experience” that will reduce checkout times by as much as half and use AI to craft personalized recommendations to consumers. Meanwhile, online payments firm AffiniPay announced Wednesday that it embedded generative AI into its legal technology offerings.
On the commerce front, Etsy launched a hub Wednesday that uses AI human curation to help shoppers find gifts for any occasion. And PYMNTS took a piercing look Monday (Jan. 22) at how retailers are folding generative AI capabilities into their 2024 playbooks.
PYMNTS Intelligence found that shopping beats out banking for consumer preference around AI-enabled experiences. And AI is increasingly giving the fitness category a workout.
To help firms more seamlessly integrate AI into their operations, OpenAI unveiled Thursday new embedding models, application programming interface usage management tools and plans to reduce pricing for one of its models.
But it’s not just commerce and checkout where AI is having an impact; the innovation is also being embedded across hardware devices.
Apple is reportedly pushing to bring AI to the iPhone, quietly making a series of acquisitions, hires and updates to its hardware. Meanwhile, Samsung earlier this month introduced its new Galaxy S series phone, billing it as a “new era of mobile AI.” It is part of what researchers believe will be a wave of more than 1 billion AI-powered smartphones expected to ship in the next three years.
Due to the volume of resources and costs involved in developing and deploying AI systems, and despite the FTC’s scrutiny, partnerships are emerging as a popular and even necessary approach to commercializing contemporary AI and building out the frontier capabilities of the technology.
Increasingly, the government itself wants to get in on the action.
The National Science Foundation announced Wednesday a federal program designed to increase access to AI resources, including tools, data and computing infrastructure, beyond just the world’s most valuable tech businesses.
The pilot program, called the National Artificial Intelligence Research Resource comes after the White House’s executive order mandating that barriers to entry to AI infrastructure be lowered. Several Big Tech companies will be tasked with providing resources, funding and tools alongside 11 federal agencies.
Elsewhere on the federal front, the White House said it wants AI to be good news for small businesses.
In the private sphere, months after investing in Hugging Face, Google launched a partnership Thursday with the open-source AI firm. The collaboration will let developers use Google Cloud infrastructure for all Hugging Face services, while also allowing for the training of Hugging Face AI models on Google Cloud.
This comes as Meta is intensifying competition in the AI market by consolidating its two advanced AI divisions — the Fundamental AI Research team and its top-level generative AI product team — into a single group.
The move underscores how Meta is now prioritizing product-level progress in developing general-purpose AI chatbots and securing top talent in the field of AI engineering — as opposed to attempting to lure top researchers to work on strategies like Meta’s metaverse, which is losing over $1 billion a month.
Organizations are determining how to move from sharpening their AI strategies to deploying them.
But when it comes to effectively deploying AI systems within the enterprise, there are some tech terms business leaders need to know, and some that they can leave to their engineering and data teams (for now).
PYMNTS wrote Tuesday about how anthropomorphizing AI systems, or attributing human-like characteristics to them, can pose several dangers. For many business use cases of the technology, doing so can serve as a fatal distraction from the utility AI can offer.
Education and communication about the nature of AI systems can help manage expectations and ensure responsible use. Within an enterprise environment, deploying AI systems with a clear-eyed approach to quantifiable goals and expected return on investment is key to success.
While news of AI that can surpass human intelligence is helping fuel the hype of the technology, the reality is far more driven by math than it is by myth.
At a fundamental level, generative AI models are built to generate reasonable continuations of text by drawing from a ranked list of words, each given different weighted probabilities based on the dataset the model was trained on.
PYMNTS reported Tuesday about how colleges and universities are increasingly weaving AI into their lesson plans.
“If you look at the maturity of AI models over the years, if you go back 20 years, AI was more around recognition, and gradually that evolved into coming up with insights and serving as a recommendation engine,” RXO Chief Information Officer Yoav Amiel told PYMNTS in an interview posted Friday (Jan. 26). “Today, AI is capable of task completion — and that’s what gets me excited.”
Finally, economist David Evans penned a piece for PYMNTS on how to think about AI regulation.
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