Disenchanting the AI Hype
It is important that we become more certain about what the field of artificial intelligence can contribute with right now and what needs further research. A very recent example could certainly be with facial recognition used in policing in the United States. IBM, then Amazon and later Microsoft decided to suspend the sales and use of these technologies for a year. When consequences can be seen more clearly the world becomes less magical for a moment, however that does not mean it has to be less meaningful.
In an article published on the 30th of May 2020 in the Wall Street Journal a journalist argues that AI has seemed like a ‘magic sauce’ to many and that it cannot solve all our problems. The article is called: AI Isn’t Magical and Won’t Help You Reopen Your Business by Christopher Mims. Here is an excerpt:
“What do you do when a sudden break from past trends profoundly reorders the way the world works? If you’re a business, one thing you probably can’t do is turn to existing artificial intelligence.
To carry out one of its primary applications, predictive analytics, today’s AI requires vast quantities of relevant data. When things change this quickly, there’s no time to gather enough. Many pre-pandemic models for many business functions are no longer useful; some might even point businesses in the wrong direction.
The article talks of the amount of hype in the industry.
“The hype around AI, among those who actually use it, is subsiding.”
Still, the author argues through conversations with Rachel Roumeliotis, a vice president at O’Reilly Media, that despite magical notions AI can be useful for mainly three reasons.
- Voice-activated personal assistants.
- Unlocking phones with faces or fingerprints.
- Assisting humans in making decisions.
On the one hand there are a few signs that the hype around AI is receding.
- Uber recently shut down its AI research lab.
- Airbnb’s layoffs included at least 29 full-time data scientists, according to its directory of those let go.
- SharpestMinds founder Edouard Harris. Hiring for such roles has slowed significantly, down by 50% since before the pandemic, he adds. On the other hand, that means there’s still demand, though it’s diminished.
On the other hand large technology companies keep hiring.
In the article the author had talked to Rajeev Sharma, head of enterprise AI at Pactera Edge, a technology-consulting firm who says:
“Now is a great time to hire them… it’s like buying discounted shares after a stock-market crash.”
At the same time one of the biggest perceived issues according to a large survey is leaders who do not appreciate the value of AI.
Gary Marcus, is a New York University professor and was formerly the head of autonomous driving at Uber. He argues most models are ‘brittle’, like: “…big engines for finding statistical correlations.”
An algorithm to predict the best way to route goods through a supply chain or the buying habits of shoppers can break down during events like the coronavirus pandemic.
What the author discusses is the attitude of certain AI researchers describing it as ‘bullish’ in regards to the long-term value of AI:
“At the Allen Institute for AI, founded by late Microsoft co-founder Paul Allen, researchers are currently applying AI to the discovery of treatments, vaccines, and clinical insights into the behavior of Covid-19” — Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence
The author that most firms may have less need for specialists who build AI than the kind of ‘day-to-day’ software engineering required to gather, clean and send data to the cloud.
He argued that the next point of development is the role known as “data engineering.” This:
“…is the next evolution of data science. These kinds of jobs don’t require as much specialized knowledge and are accessible to a much broader array of people with coding experience.”
The journalist in question has a great array of writing experience within technology, and the text does drive home an important point: AI cannot solve anything.
However, it can be a great contribution in a variety of processes.
This is #500daysofAI and you are reading article 380. I am writing one new article about or related to artificial intelligence every day for 500 days.