My annual (I’ve done it twice now, so that now fits) has been posted. I review some of the key stories I covered in 2018 and make some basic predictions for what will happen in the near-term future. Hint: Aritifical intelligence and machine learning are still early in the adoption lifecycle, but cloud technologies mean faster adoption curves than in the past.
A couple of new articles over on the article page.
The first it almost a counter argument to the data problem I’ve previously discussed. It’s not enough to have good data if people can’t trust the algorithm.
Then, at SAP TechEd 2018, Bernd, Leukert’s keynote speech had one key element that showed me that the company was finally internalizing the ecosystem focus needed to thrive in the cloud versus the on-premises model they’ve used for decades.
I’ve published another article in my Management AI series that’s a subset of my forbes.com column. This one is a high level introduction to natural language processing (NLP) and natural language generation (NLG).
Data discovery, as we defined it in the 1990s, has changed to take advantage of modern algorithms. It’s moved past basic data analysis and is better at finding relationships between different pieces of information. Discussed in detail in my forbes.com article.
My latest article in Search Data Management points out the importance of data quality in training machine learning systems.
My latest article on Forbes describes how different components of AI are beginning to work together to improve manufacturing.
Here’s an article on Forbes about Tableau buying Empirical Systems.
My focus on business intelligence the last few years, my long term interest in artificial intelligence and the growth of machine learning came together to drive the content for my latest Forbes article.
In my work with TIRIAS Research, I’m covering machine learning. As part of that, I am publishing articles on Forbes. One thing I’ve started this month, with two articles, is a thread on management AI. The purpose is to take specific parts of AI and machine learning that are often described very technically, and present them in a way that management can understand what they are and, more importantly, why they provide value to decision making.
VentureBeat hosted a webinar that missed the mark in the title, but is still worth a watch for those interested in how technology is changing the banking industry. Artificial intelligence (AI) was only discussed a few times, but the overarching discussion of the relationship between the young financial technology (fintech) companies and the existing banking infrastructure was of great value.
The speakers were:
The opening question was about the relationship between fintech and banking organizations. The general response was that the current maturity of fintech means that most companies are focusing on one or two specific products or services, while banks are the broad spectrum organizations who will leverage that to provide the solutions to customers. Katy Gibson did point out that while Yodlee does focus on B2B, other fintech companies are trying to go B2C and we’ll have to see how that works out. Dion Lisle suggests that he sees the industry maturing for the next 18-24 months, then expects to see mergers and acquisitions start to consolidate the two types of businesses.
One of the few AI questions, one on how it will be incorporated, brought a clear response from Ms. Gibson. Just as other companies have begun to realize as machine learning and other AI applications begin to be operationalized, clean data is just as important as it always has been. She points out that banking information comes from multiple sources, isn’t clean and is noisy. Organizations are going to have to spend a lot of time and planning to ensure that the systems will be able to be fed useable information that provides accurate insight.
There was an interesting AI-adjacent question, one where I’m not sure I agree with the panelists. Imagine a consumer at home, querying Alexa, Siri, or other AI voice system and asking a financial question, one such as whether or not personal financial systems are good to buy a specific item. If the answer that comes back is wrong, who will the consumer blame?
The panelist consensus seems to be that they will blame the financial institution. I’m not so sure. Most people are direct. They blame the person (or voice system) in front of them. That’s one reason why customer support call centers have high turnover. The manufacturing system might be to blame for a product failure, but it’s the person on the other end of the line who receives the anger. The home AI companies will need to work with all the service providers, not just in fintech, to ensure not just legal agreements specify responsibility, but that also the voice response reflects the appropriate agreements.
The final item I’ll discuss was a key AI issue. The example discussed was a hypothetical where training figured out that blue eyed people default on home loans more often. What are the legal ramifications of such analysis. I think it was Dion (apologies if it was someone else), pointed out the key statistical statement about correlation not meaning causality. It’s one thing to recognize a relationship, it’s another to assume one thing causes another.
Katy Gibson went further into the AI side and pointed out that fintech requires supervised learning in the training of machine systems. It’s not just the pure correlation/causality issues that matter. Legal requirements specify anti-discrimination measures. That means that unsupervised learning is not just finding false links, it could be finding illegal ones. Supervised learning means data sets including valid and invalid results must be used to ensure the system is trained for the real world.
There were more topics discussed, including an important one about who owns privacy, but they weren’t related to AI.
It was an interested webinar with my usual complaint about large panels: There were too many people for the short time. All of these folks were interesting, but smaller groups and a more tightly focused discussion would have better served the audience.