At Rise of AI 2018 in Berlin, Rick Newman, IBM's worldwide AI sales leader, argues that organizations should start AI projects with a narrow, proven use case rather than attempting to transform an entire company at once. He illustrates the power and risk of predictive models with an example, based on a real case, in which a bank used historical loan data to flag customers likely to divorce, and therefore likely to default, with about 80 percent certainty. Citing IDC and Gartner research, he says 80 percent of a data scientist's time typically goes into preparing data, and describes IBM tools built to automate that work, including one that let a non-specialist build a cancer-cell detection model reaching 95 percent accuracy in hours. He argues machine learning remains essential for traceability in regulated settings such as financial services, and describes IBM's NVLink-based Power CPUs and distributed deep learning cutting a breast-cancer research customer's training window from days to hours. He closes with an insurance example from his own life and a warning against proprietary, non-portable technology.
Key takeaways
- Newman advises starting AI initiatives with a narrow, proven use case rather than an organization-wide transformation.
- He cites IDC and Gartner figures that 80 percent of a data scientist's time goes into data preparation, not modeling.
- A non-data-scientist colleague built a cancer-cell detection model reaching 95 percent accuracy in a few hours using an IBM tool.
- IBM's NVLink-based Power CPUs and distributed deep learning cut one breast-cancer research customer's training window from days to about 11 hours.
- Traceability back to a model's origin remains essential for compliance, which is why Newman says machine learning is 'not dead.'
Detailed notes
Start small and treat data science as a team sport
Newman's core advice is to begin AI projects with a proven, narrow use case rather than attempting to transform an entire organization at once, since broad AI transformation projects tend to fail. He illustrates the value and risk of predictive models with an example, based on a real case, in which a bank analyzed historical loan data and found that customers who divorced were far more likely to default, building a model that flags such customers with about 80 percent certainty; he stresses this kind of analysis has real social implications even though a loan officer would never say so directly. He calls data science 'a team sport' and warns that many self-described data scientists are unqualified, recommending buyers make sure any hire or consultant is genuinely skilled rather than, as he puts it, someone who was an Oracle database administrator the day before.
Where the time actually goes
Citing IDC and Gartner figures, Newman says 80 percent of a data scientist's time is typically spent preparing data rather than building models, which is why IBM has built automation tools, including one that uses Spark under the hood to normalize data. He describes a tool that let a non-data-scientist colleague build an image-recognition model detecting cancerous cells with 95 percent accuracy in a few hours instead of weeks, comparing that figure to benchmarks he cites for human oncologists: about 95 percent for a researcher focused solely on cell analysis, and about 80 percent for a practicing oncologist reviewing 20 to 30 cases a day.
Machine learning is not dead
Despite IBM's growing focus on deep learning tools, Newman insists machine learning still matters, particularly where a decision must be traceable back to its origin, such as when regulators investigate a specific financial trade for possible insider information; retrained models without a preserved snapshot of the prior version can make that traceability impossible.
Scaling training with distributed and large-memory systems
Newman describes an IBM announcement, made about three weeks before this talk at IBM Think in Las Vegas, showing logistic regression parallelized across GPUs without code changes and beating the previous performance record by 46 times, with a similar 45-times acceleration achieved for distributed deep learning, due to enter technical preview in June 2018. He also describes IBM's integration of NVIDIA's NVLink directly on its Power CPUs, giving access to a coherent memory space of up to two terabytes and, he says, roughly 95 percent scalability up to 256 GPUs; he cites one US breast cancer research customer whose training window fell from several days to around 11 hours as a result, without changing their code.
Two real-world impacts: insurance and cancer screening
Newman contrasts his own roughly 11-hour experience getting a car accident claim processed across two garages with a friend's insurer, where a photograph of the damage triggers a deep learning estimate and repair-shop recommendation within minutes, a difference he says will make him switch insurers. He also describes a breast-cancer customer aiming to give every specialist at least 15 minutes with a patient, versus the roughly three-minute US average, by using AI to let non-diagnostic patients be cleared by a nurse so specialists can focus on patients flagged with 92 percent certainty as likely positive. He closes by urging the audience to avoid building or depending on proprietary, non-portable technology stacks.
Quotes
No, machine learning is not dead.
Rick Newman
Avoid tinkering. Let people who are in the business of creating tools create the tools.
Rick Newman
Questions answered in this talk
How much of a data scientist's time is typically spent on data preparation?
According to Rick Newman at Rise of AI 2018, citing IDC and Gartner research, roughly 80 percent of a data scientist's time goes into preparing and normalizing data rather than building models.
How did IBM help a customer speed up breast cancer research training?
Rick Newman said at Rise of AI 2018 that IBM's NVLink-based Power CPUs and distributed deep learning tools cut one breast cancer research customer's multi-day GPU training window down to around 11 hours without any code changes.
Why does Rick Newman say machine learning is not dead despite the rise of deep learning?
At Rise of AI 2018, Rick Newman argues machine learning remains essential because some use cases, such as financial services compliance, require tracing a decision back to its origin, which is harder once deep learning models are retrained without a preserved snapshot.