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AI-SDV 2021: Nils Newmann - AI – Who is in control and why is that important?

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AI-SDV 2021: Nils Newmann - AI – Who is in control and why is that important?

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Since the 1950s, AI has been plagued by overpromise and underperformance, particularly at the interface between AI and topical Subject Matter Experts (SMEs). AI has struggled to produce results that SMEs deem effective. As the current hype around the most recent wave of AI recedes, it is time to assess if the new round of research has improved AI’s capacity to help SMEs. This presentation looks at three aspects of current AI research that might actually be useful: Composite AI, Generative AI, and Small Data. These three approaches have the capacity to reduce the distance between AI systems and SMEs by allowing experts to have more local control and input into the behavior of AI systems. This closer interaction has the potential to lead to useful, effective results for SMEs.

Since the 1950s, AI has been plagued by overpromise and underperformance, particularly at the interface between AI and topical Subject Matter Experts (SMEs). AI has struggled to produce results that SMEs deem effective. As the current hype around the most recent wave of AI recedes, it is time to assess if the new round of research has improved AI’s capacity to help SMEs. This presentation looks at three aspects of current AI research that might actually be useful: Composite AI, Generative AI, and Small Data. These three approaches have the capacity to reduce the distance between AI systems and SMEs by allowing experts to have more local control and input into the behavior of AI systems. This closer interaction has the potential to lead to useful, effective results for SMEs.

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AI-SDV 2021: Nils Newmann - AI – Who is in control and why is that important?

  1. 1. Copyright ©1997 - 20 21Search Technology, Inc. TheVantagePoint.com | 1 Nils Newman | October 5, 2021 AI Who is in Control and Why is that Important
  2. 2. Copyright ©1997 - 20 21Search Technology, Inc. TheVantagePoint.com | 2 The Magic of AI • AI is often sold as a magical tool • Click a button - - - get an answer • Better yet – no clicking required • AI just does the work for you…
  3. 3. Copyright ©1997 - 20 21Search Technology, Inc. TheVantagePoint.com | 3 The Reality • AI can work like magic But… .. • Behind the scenes, a lot of work goes into making these systems work
  4. 4. Copyright ©1997 - 20 21Search Technology, Inc. TheVantagePoint.com | 4 An Example • A university is tasked to build an AI system which reads articles and automatically tags them as AI articles or not. • The system then further tags the AI articles based on seven sub- domains in AI. • The team starts with Google BERT (Bidirectional Encoder Representations from Transformers). • Then moves to SciBERT – the BERT trained on scientific publications. • Next they modify SciBERT by using tens of thousands of manually human tagged AI records so their AI- SciBERT would “understand” the categories of AI they were trying to find.
  5. 5. Copyright ©1997 - 20 21Search Technology, Inc. TheVantagePoint.com | 5 An Example: continued • Next they build another AI model that could adapt as terms changed over time so the system would be robust in the future. • Finally, they run the model against millions of publications from different sources. • The system produces F- scores above 0.8 for most AI categories with no human intervention. • So… . Magic! Plus a big budget for manual tagging.
  6. 6. Copyright ©1997 - 20 21Search Technology, Inc. TheVantagePoint.com | 6 What people ignore about AI • The training piece of AI, whether it is unsupervised, semi- supervised, or supervised, is the bit that most people tend to gloss over when talking about AI. • Training is also one of the biggest barriers to the wider deployment of AI. • It is one of the big reasons why people are suspicious about AI. Things do not work because the training is poor. • Ignoring the training issue contributes to the overpromise and underperformance of AI.
  7. 7. Copyright ©1997 - 20 21Search Technology, Inc. TheVantagePoint.com | 7 The SME’s • Areas where specialized subject matter experts contribute to knowledge work have been particularly problematic for AI. • The problem is not that AI is not useful. • AI can be very useful to SME’s, but previous generations of AI have not addressed the SME’s primary issue… .
  8. 8. Copyright ©1997 - 20 21Search Technology, Inc. TheVantagePoint.com | 8 SCALE: Small Data • SME’s typically operate on Small Data. • A researcher at a chemical company might use patent information that has been categorized using Big Data AI such as Patent Classifications or Chemical Tagging. • But for the impactful work, the SME usually operates at the Small Data level – searches of a few thousand records. • At the Small Data level, the SME has to be in control of the training process.
  9. 9. Copyright ©1997 - 20 21Search Technology, Inc. TheVantagePoint.com | 9 The problem • Big Data algorithms do not work in a small data world. • For example, most Deep Learning algorithms need training sets in the 10,000’s or 100,000’s records. • These types of approaches simply will never work for the SME who uses 100’s or 1,000’s of records. • Big Data algorithms also mean the SME has little control over the process. • They just use the output and have little control over training.
  10. 10. Copyright ©1997 - 20 21Search Technology, Inc. TheVantagePoint.com | 10 Some Learning Solutions… • Fortunately, this recent round of AI development has produced some tools to allow developers to create effective AI for the SME working with Small Data. • Few- Shot Learning  Deployed in VP’s Smart Trainer • Zero- Shot Learning  Deployed in VP’s Find Similar Records • Transfer Learning  Portable Knowledge Bases in VP
  11. 11. Copyright ©1997 - 20 21Search Technology, Inc. TheVantagePoint.com | 11 More Solutions…Composite AI • In addition to new algorithms that require only small training sets, the latest round of AI has produced improvements in how AI algorithms work with each other to form Composite AI. • In the past, most AI systems relied on only one approach. • Now different AI systems can work together to address different aspects of a problem. • For example, VantagePoint uses around half a dozen different AI approaches.
  12. 12. Copyright ©1997 - 20 21Search Technology, Inc. TheVantagePoint.com | 12 The Possible Future: Generative AI • Generative AI is AI that creates something new from what it has already learned. • Current models typically require very large training sets so are currently of limited utility in a Small Data world. • But, we have experimented with using Generative AI to write a summary abstract about a collection of documents. • This opens the possibility of using Generative AI to create something approaching a proxy for “conceptual understanding”. • This is much work to do, but we see some interesting potential for Generative AI in a Small Data world.
  13. 13. Copyright ©1997 - 20 21Search Technology, Inc. TheVantagePoint.com | 13 AI, Small Data, and SME’s • Many Big Data AI designers are trying to replace humans. • The work of SME’s is too important and too “human” to be simply replaced by AI. • AI can make the SME’s life easier and allow them to be more effective in their work. • But the design of the AI systems in the Small Data space has to start with the human and the recognition of the value of human expertise. • Regardless of which AI approaches are deployed, in the Small Data space, the Subject Matter Expert has to be in control.
  14. 14. Copyright ©1997 - 20 21Search Technology, Inc. TheVantagePoint.com | 14 Questions?
  15. 15. Copyright ©1997 - 20 21Search Technology, Inc. TheVantagePoint.com | 15

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