Lately I’ve been thinking a lot about where AI fits into corporate strategy and structure. Marc Andreessen recently suggested

…there’s two obvious business models… one is to be a horizontal platform provider [or] infrastructure provider, analogous to the operating system or the database for the cloud. The other opportunity is in the verticals, the applications of AI.

…AI is a platform and an architecture, in the same sense that the mainframe was architecture, the mini computer was an architecture, the PC, the internet, the cloud, have been architectures. We think there are very good odds that AI is the next one of those. When there’s an architecture shift in our business…everything above the architecture gets rebuilt from scratch. Because the fundamental assumptions about what you’re building change. You’re no longer building a website, you’re no longer building a mobile app, you’re no longer building any of those things, you’re building instead an AI engine that is, in the ideal case, giving you the answer to whatever the question is. And if that’s the case, then basically all applications will change. Along with that all infrastructure will change. Basically, the entire industry will turn over again, the same way that it did with the internet, and the same way it did with mobile and cloud. And so if that’s the case, then it’s just it’s going to be like an absolute explosive period of growth for this entire industry.

What does this mean for business? It means incumbents are displaced because their products are no longer relevant. And the products become irrelevant because the service that was being performed is now being delivered in a new way, perhaps by an AI engine running across the organization. Or perhaps the service is no longer being delivered at all because it has become redundant in a world wherein AI engines deliver superior services to what was previously being delivered.

If AI is a feature sitting on top of an already existing underlying service, then AI will eventually be added to most products and services. But it will sit on top of the service and the underlying service won’t actually change much.

I see AI as much more transformational. I see AI reframing the underlying service. In this way, AI creates new services. Entirely different services and experiences than what was previously being offered.

The Bureau of Industry and Security (BIS) issued an interim final rule that imposes a license requirement for the export and reexport of software specially designed to automate the analysis of geospatial imagery. The rule applies to exports and reexports to all countries beyond Canada.

The rule is scheduled to be published on Monday January 6, 2020. But you can read the unpublished rule here.

Breast cancer is the most common cancer in women worldwide. Early detection and treatment can lower mortality rates. But clinicians still fail to identify breast cancer about 20 percent of the time (false-negative results). Clinicians also identify cancer, when there is no breast cancer present (false-positive results). Studies suggest 7-12 percent of women will receive false positive results after one mammogram and after 10 years of annual screening, more than half of women will receive at least one false-positive recall.

False-negative results provide a false sense of security and could ultimately hinder treatment effectiveness. False-positive results can cause anxiety and lead to unnecessary tests and procedures. Another hurdle in identifying breast cancer is a shortage of radiologists needed to read mammograms.

Researchers have developed an AI system that surpasses human experts in breast cancer identification. Their study results were recently published in the journal Nature.

We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers…We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%.

The study results are promising. The AI system outperformed six radiologists and also lowered missed cancer diagnoses on the U.S. sample by 9 percent and mistaken readings of breast cancer by 6 percent. It also produced results across populations, something many AI systems have yet to produce. The researchers didn’t go as far as to suggest their AI system would replace humans.

The optimal use of the AI system within clinical workflows remains to be determined. The specificity advantage exhibited by the system suggests that it could help to reduce recall rates and unnecessary biopsies. The improvement in sensitivity exhibited in the US data shows that the AI system may be capable of detecting cancers earlier than the standard of care. An analysis of the localization performance of the AI system suggests it holds early promise for flagging suspicious regions for review by experts.

Beyond improving reader performance, the technology described here may have a number of other clinical applications. Through simulation, we suggest how the system could obviate the need for double reading in 88% of UK screening cases, while maintaining a similar level of accuracy to the standard protocol. We also explore how high-confidence operating points can be used to triage high-risk cases and dismiss low-risk cases. These analyses highlight the potential of this technology to deliver screening results in a sustainable manner despite workforce shortages in countries such as the UK.

At the same time, it becomes more difficult to make the case for approaches that are exclusively human. It is hard to imagine patients, insurance companies, and others won’t demand AI systems augment what humans are doing. This is especially true in healthcare. But will also likely become increasingly true in other domains. What tasks would you want humans to do alone if you know that you can get better results (greater accuracy, faster, etc) when human capability is augmented with AI systems.

Humans will need to learn how to incorporate these type of AI systems into their workflow. The next big step for AI seems to be “operationalizing AI.” This is likely a decade in the works, but slowly you will see individuals figuring how to best work within environments that are being redefined by AI systems.