My annual end-of-the-year post “52 Things I Learned About Technology” will publish tomorrow and a good chunk is devoted to AI. But a few things hit the grid this week that deserve special mention.
19 December 2019 (Paris, France) – We all know AI is a hot topic. I began writing about it 8 years ago. And I’ve noted that before we start talking about AI, we have to first understand and watch how machine learning as technology develops. Staying current is a full-time job.
The desire to use AI/ML in the enterprise specifically is high. I’ve seen several IT surveys this year and consistently the results highlight over 80% of those surveyed say they are either actively deploying some kind of AI or looking to in the next six months. But as this report highlights, it is still a difficult endeavor.
Algorithmia, which found that while machine learning maturity in the enterprise is generally increasing, the majority of companies (50%) spend between 8 and 90 days deploying a single machine learning model (with 18% taking longer than 90 days).
There are many challenges that explain this issue, plenty I don’t have time to dive into, but it is worth knowing that analytics is the most common area where companies say they have AI deployed. Most still require a team of data scientists, but this is why technologies in the works from Microsoft, Google, and Amazon to automate AI as a part of the analytics process and lessen the need for data scientists is important. Automated AI is a big issue, and I’ll explore that tomorrow in my wrap-up.
Understanding the challenges of deploying AI is also a factor in understanding why Intel purchased Habana Labs this week. We are nowhere near where we need to be when it comes to the infrastructure for AI. This includes the silicon, the software, the connection between the edge device and the cloud, and the way data sets are managed and preserved so the computer can be trained.
All of this to say, we are still in the early stages of AI/ML, and we have a long road of innovation ahead from every touchpoint in the system. This is also why you should expect to see quality AI startups be snatched up quite quickly by large companies due to the competitive nature and critically importance to the future that is AI.
And be wary of the media hype. Journalists shape the debate around the potential benefits and risks of artificial intelligence. But which experts so they use? This week the Reuters Institute/Oxford University research team looked at which experts British and American journalists turn to when reporting on AI, and whether these scholars are tied to industry. The factsheet can be found by clicking here. It is a long read so I’ll highlight the key findings:
- Just a few scholars account for a disproportionate share of news mentions: the 10 most mentioned scholars account for 70% of overall news mentions.
- Those who appear most frequently in the news are not necessarily those who are widely cited by academic peers: the 16% of scholars affiliated with industry account for 65% of all news mentions. Scholars responsible for over half of all Google Scholar citations have no news mentions at all.
- Most of the experts quoted are men. Just 6% of identified scholars are women. They account for 6.7% of news mentions and 4.2% of citations.
The implications are obvious. The research raises questions about the media’s reliance on a relatively narrow band of artificial intelligence experts and the reasons behind this. The authors of the study consider what this means for news audiences’ understanding of this key issue.
Much more on AI tomorrow when I publish “Looking back: 10 major things I learned about technology in the past decade … plus 52 things I learned this past year”
