In all the frenzied discourse about large language models (LLMs) such as GPT-4 there is one point on which everyone seems to agree: these models are essentially stochastic parrots – namely, machines that are good at generating convincing sentences, but do not actually understand the meaning of the language they are processing. They have somehow "read" (that is, ingested) everything ever published in machine-readable form and create sentences word by word, at each point making a statistical guess of "what one might expect someone to write after seeing what people have written on billions of webpages, etc". Ever since ChatGPT arrived last November, people have been astonished by the capabilities of these parrots – how humanlike they seem to be and so on. But consolation was drawn initially from the thought that since the models were drawing only on what already resided in their capacious memories, then they couldn't be genuinely original: they would just regurgitate the conventional wisdom embedded in their training data. That comforting thought didn't last long, though, as experimenters kept finding startling and unpredictable behaviours of LLMs – facets now labelled "emergent abilities".

When we talk about artificial intelligence, we rely on metaphor, as we always do when dealing with something new and unfamiliar. Metaphors are, by their nature, imperfect, but we still need to choose them carefully, because bad ones can lead us astray. For example, it's become very common to compare powerful A.I.s to genies in fairy tales. The metaphor is meant to highlight the difficulty of making powerful entities obey your commands; the computer scientist Stuart Russell has cited the parable of King Midas, who demanded that everything he touched turn into gold, to illustrate the dangers of an A.I. doing what you tell it to do instead of what you want it to do. There are multiple problems with this metaphor, but one of them is that it derives the wrong lessons from the tale to which it refers.

AI is approaching the next level of maturity, coming out of the hype cycle, says Gartner. Its adoption is expanding across industries beyond automation to building new-generation intelligent products and services for business growth. However, half of them acknowledge that they don't have skilled talent to make the most of AI advances. This is where experienced AI consulting firms come in to help. The market of AI consulting is vast, ranging from tech giants like IBM and Accenture to Big 4 firms and smaller-scale innovators.

Insite AI, a platform for consumer packaged goods companies that uses AI to provide recommendations on how to price, distribute and promote their products in physical stores, today launched out of stealth with $19 million in Series A capital from NewRoad Capital and M12, Microsoft's corporate venture arm. Co-founder Shaveer Mirpuri says that the funding will be put toward customer onboarding, building a team of industry experts to help shape product initiatives and an expanded feature set. Mirpuri and Jonathan Reid co-launched Insite with the belief there was a large addressable market for brick-and-mortar sales revenue growth management software. It's true retailers -- and by extension, brands -- face considerable challenges in this area, particularly as the economy takes a precipitous turn. According to NPD, more than 80% of U.S. consumers said in May that they'd rein in product spending within the next three to six months.

The Organisation for Economic Co-operation and Development is an intergovernmental agency with 38 member countries, founded in 1961 in France to stimulate economic progress and world trade. The OECD's "AI Policy Observatory" tracks artificial intelligence research and advancement. This includes the number of annual AI-related research papers by country, an indicator of its progress. According to the OECD, since 2010 China has led the world in AI research, reaching 141,006 cumulative (since 1980) papers in 2021. Hence China is becoming the global leader in artificial intelligence.

By raising awareness about the different pathways into AI and making it more accessible, we want to inspire participation from historically underrepresented groups so that together we can build a more equitable and ethical tech future. AI Ethics and Policy Researcher, Favour Borokini takes us through her career journey and shares what inspired her to join this space and how she landed her current role at Pollicy. She also talks about some of the most common barriers and challenges she tackles on a daily basis and how she deals with them as someone who comes from a non-technical background. She also shares her thoughts on diversity and the most practical tips to get started in this space especially if you're someone who comes from a non-technical background. You can listen to the podcast or read through their conversation below.

For over 100 years, the world of automobiles has had the road to itself -- now with the digitization of everything, data is starting to rule the road. IoT is touching almost everything in both the commercial and consumer worlds. In parallel, the automotive industry is undergoing a major rebirth, in some cases kicking and screaming. Where the automakers (in their parlance, OEMs) have had over 100 years of self-centricity -- and not having to deal with "ecosystems" other than their respective galaxies of suppliers and dealers -- they're now grappling with a sea change being caused by, well, the IoT. Some are already calling it "the Internet of Moving Things" and why not?

The Future of Work has transformed. COVID-19 has made a permanent impact on how we work, where we work and when we work. Employee attitudes towards companies have drastically changed with regards to social, ethical, environmental, diversity and governance behaviors and practices. The business world is undergoing a period of unrivalled change. To lead in the path forward we must move beyond business as usual, companies must drastically change course to attract and retain top talent, meaningfully address ESG problems, make cultural changes to support the future and address all of the gaps identified during the pandemic.

You might think it foolhardy to make predictions about something that changes as furiously fast as emerging technology. Driven by massive investments, competing for a market that appears limitless, thousands of companies innovate constantly, sometimes at cross purposes. Yet we are human, after all, so we can't resist peering into the unknowable future and proclaiming, "Yes, this is what's ahead." Plus it's that time of year: in January we are (we hope) imbued with a fresh energy that allows us to accurately assess the year ahead. Not that all annual predictions are mere guesses. Clearly, the tech forecasters below are top practitioners who are close to their respective sectors. They're the experts who have long labored in cloud, AI, security, edge, data analytics, digital transformation, accruing years of deep market experience. If anyone can foresee the future, it's these seasoned thought leaders.

Strategy Development has followed a set path since the last century where a predetermined, rectilinear, and inflexible approach defined the process. In the 21st century, however, business leaders are devising Strategy by evolving it into a probabilistic, repeated, and multifaceted process. An approach that can both endure and adapt to the growing pace of Change and Disruption that is manifesting itself in all industries. Using gaming, AI, unremitting execution, and adjustment, with numerous scenarios to deliberate on, leaders create "Flywheels" that successfully tackle the not so deterministic world where the future is highly uncertain. Flywheel is a concept originally used in the power industry to explain an origin of stabilization, energy storage, and momentum.