What to do when surrounded by people who are losing their minds about the Newest New Thing? Answer: reach for the Gartner Hype Cycle, an ingenious diagram that maps the progress of an emerging technology through five phases: the "technology trigger", which is followed by a rapid rise to the "peak of inflated expectations"; this is succeeded by a rapid decline into the "trough of disillusionment", after which begins a gentle climb up the "slope of enlightenment" – before eventually (often years or decades later) reaching the "plateau of productivity". Given the current hysteria about AI, I thought I'd check to see where it is on the chart. It shows that generative AI (the polite term for ChatGPT and co) has just reached the peak of inflated expectations. That squares with the fevered predictions of the tech industry (not to mention governments) that AI will be transformative and will soon be ubiquitous.

According to the Gartner hype cycle, the least amount of time a product takes to hit the'plateau' of expectations is two years. The hot chatbot has shattered all records of a product lifecycle, going through all stages of the cycle within 3 months. Launched in November-end last year, ChatGPT has already been through the innovation trigger, inflated expectations, disillusionment, enlightenment, and is now reaching a mature period of measured expectations, leading to industry adoption. A contributing factor to this might be the bot's meteoric growth, which scaled to 10 million users within 40 days. For contrast, Instagram took almost a year to reach the same milestone.

An Alphabet (GOOGL) unit used artificial intelligence to disrupt the way new drugs are discovered. This is the best way for investors to take advantage. DeepMind announced July 28 the availability of a database of nearly all of the proteins known to science. The open source project eliminates one of the mostly costly parts of biotechnology. Investors should consider buying Ark Genomic Revolution ETF (ARKG).

Contemplating possible future scenarios should be left to the field of philosophy, not to futurology – or so claims Luciano Floridi in this somewhat harsh but fair editor letter. Floridi examines so-called Artificial Intelligence (AI) winters and their impact on the development of AI. AI winters are periods where the hype for AI wanes and often the result of disillusionment when – inevitably – promises concerning AI applications fail to deliver. The drawbacks of hype for AI are twofold: on the one hand, it makes people more skeptical towards useful applications, and on the other hand alarmism blinds people to the actual risks associated with AI applications. Floridi mentions these instances of alarmism during his discussion of hype, seemingly categorizing alarmism as an instance of hype rather than a different variant of an exaggerated claim.

Edward Rothberg is CEO and Co-Founder of Gurobi Optimization, which produces the world's fastest mathematical optimization solver. This is a question that -- as the CEO of a mathematical optimization software company -- I get asked all the time. Although it seems like a simple question, it's actually quite difficult to come up with a concise, coherent answer. Indeed, mathematical optimization and machine learning are two tools that at first glance -- like scissors and pliers -- may seem to have a lot in common. When you look closely at their fundamental features and actual applications, however, you'll see some important differences.

The term AI Winter, first appeared in 1984 having been discussed at the American Association of Artificial Intelligence. This discussion then saw a rise in pessimism and a reduction of funding. Many minds that had survived the first'winter', prior to it being cited as such, suggested that an increase in enthusiasm for AI, perhaps without the technical capabilities to match, has seen a sharp rise and later collapse. Having hit extreme lows in the early 1990s, the enthusiasm for AI began to rise again, and as they say, the rest is now history with it now so ubiquitous in society. That said, we have seen this year that anything is certainly possible, therefore, we thought we would ask our community of AI expert friends what they thought on the topic, asking - 'Do you think we will see another AI Winter?

Cory Doctorow, 49, is a British-Canadian blogger, science fiction author and tech activist. He has held various academic posts and is a visiting professor of the Open University. His latest novel, Attack Surface, was published earlier this month. The protagonist in your new novel tries to offset her job at a tech company where she is working for a repressive regime by helping some of its targets evade detection. Do you think many Silicon Valley employees feel uneasy about their work?

There will be no data science job listings in about 10 years, and here is why. There are no MBA jobs in 2019, just like there are no computer science jobs. MBAs, computer science degrees and data science degrees are degrees, not jobs. I believe the reason companies are hiring people into data science job titles is because they recognize there are emerging trends (cloud computing, big data, AI, machine learning), and they want to invest in them. There is evidence to suggest this is a temporary phenomenon, though, which is a normal part of the technology hype cycle.

Three reports, three pieces of evidence that the fourth industrial revolution is gathering steam. See it in terms of the Gartner Hype Cycle, which divides the technology cycle into stages around initial hype, disillusionment, before the technology finally starts to fulfil potential. There has been no shortage of commentary that AI is little more than a buzzword, automation technologies such as RPA are over hyped, while healthtech has often failed to live up to expectations. Then again, it always is thus, new technology often does attract a lot of wild claims, leading to some kind of crash, before it finally fulfils potential. Maybe we are finally reaching that point when potential is realised.

As fans of Blade Runner and The Terminator can testify, AI and particularly AI disaster scenarios are a subject most people can't get enough of. Therefore, when AI technology and investment began to grow, it quickly caught the public's imagination. The problem is that the technology hasn't kept pace. The resulting gap between the perception and the reality has led us to enter what the Gartner Hype Cycle calls'the trough of disillusionment' with AI. This is especially apparent in the marketing industry, where, for all the talk of AI at industry events and in the media, only 27% of marketers in the UK are actually using it in their jobs.