Is It Hype If It's Real?
Amazing examples of AI technology are everywhere. And champions imagine even more: the full article from which this week's lead quote is taken claims that "the artificial intelligence decision-making process should be viewed as an institution".
Wow. The second quote concerns an "AI-bank", a bank is an institution.
Getting to institution or just plain "everyday usage" though is not for the faint of heart. This week's Data Decisioning Market Scan turns up several examples of the gap between "AI plumbing" and "AI delivery". The plumbing seems to be improving. But the insights still "leave a lot to be desired".
One avenue that shows promise is data sharing. As Bill Schmarzo emphasized in his recent podcast with us, the economics of data re-use is extremely attractive. The benefit of data re-use can be magnified even more via shared data.
What about data science then? Google's Cassie Kozyrkov asks a bold question: "Is data science a bubble?" Dr. Kozyrkov says "no", but that the title (and role) may change.
We add that the market for management technologies is also evolving to deliver productized tools to the huge number of organizations that will never be able to afford their own data scientists. That's good news for just about everyone ♦
"Clearly, it is possible to use AI technology to advance societal objectives." Tetyana (Tanya) Krupiy - Computer Law & Security Review - A vulnerability analysis: Theorising the impact of artificial intelligence decision-making processes on individuals, society and human diversity from a social justice perspective
"The AI-first bank will offer propositions and experiences that are intelligent (that is, recommending actions, anticipating and automating key decisions or tasks), personalized (that is, relevant and timely, and based on a detailed understanding of customers’ past behavior and context), and truly omnichannel (seamlessly spanning the physical and online contexts across multiple devices, and delivering a consistent experience) and that blend banking capabilities with relevant products and services beyond banking."
Suparna Biswas, Brant Carson, Violet Chung, Shwaitang Singh, and Renny Thomas - McKinsey & Co. - AI-bank of the future: Can banks meet the AI challenge?
"However, occasionally, change is so sudden and seismic that evolution must make way for revolution. A sharp ‘pivot’ is necessary ... In some cases, COVID-19 has sent organizations over the edge ... Other organizations are more fortunate, because they have the wisdom to put data at the heart of their decision-making and they are prepared to pivot sharply based on this data analytics."
Scott Castle - AiThority - How To Find Your ‘Perfect Pivot’ Using Data Analytics
"We argue that the goal of personalised medical care faces serious challenges, many of which cannot be addressed through algorithmic complexity, and call for collaboration between traditional methodologists and experts in medical machine learning to avoid extensive research waste."
Jack Wilkinson et. al. - The Lancet - Time to reality check the promises of machine learning-powered precision medicine
"The explosion of Big Data was predicated on capturing as much data as possible regardless of format to power the business both from an operrational or analytical perspective. The central thought was that with more data, we'd have better insights. For the most part, we've solved the big data storage and processing problems but the derivation of insights still leaves a lot to be desired."
Christian Tan - Sonder - The Future of Big Data
"... there is an incredible amount of hype about artificial intelligence today. Businesses across the world are looking to this technology with the hopes of gaining a competitive advantage over their competitors, reducing operating costs, and improving customer experience. However, not all companies are ready to leverage AI."
Kimberly D. Elsbach and Ileana Stigliani - MITSloan Management Review - Evaluating New Technology? You’re More Biased Than You May Realize
"We humans tend to overestimate artificial intelligence advances and underestimate the complexity of our own intelligence."
Melanie Mitchell via Tom Siegfried - Knowable Magazine - Why some artificial intelligence is smart until it's dumb
"Therefore, the job of data scientists (in the DNA case, the job of bioinformatics experts) is to decode the data and to find the knowledge in the data—i.e., to find the model in the data, because the data is the model."
Kirk Borne - Booz Allen Hamilton - KIRK BORNE ON BUILDING DATA SCIENCE MODELS
"The future of AI depends on shifting from emphasis on proprietary data sets, to the sharing of data across entities for knowledge creation."
Tomer Y. Avni - The Next Web - The AI landscape is shifting from ‘data’ to ‘knowledge.’ Here’s why that matters
"Data sharing is an important mechanism to ensure that, after the expensive process of data curation and producing high-quality labels is completed, results from artificial intelligence models can be reliably reproduced and the data can be used by other researchers, therefore, having the widest possible impact by vastly multiplying the number of studies that can be performed."
Sophia Y. Wang , Suzann Pershing and Aaron Y. Lee - Current Opinion in Opthamology - Big data requirements for artificial intelligence
"According to investment banking industry officials on Sept. 15, big data-related companies such as VAIV Company Inc., Alchera Inc., AgileSoDA Co. and XIILab Co. are preparing to go public by the end of this year and big data IPOs are expected to rise in coming quarters."
Ye-Jin Jun - Korean Investors - Korea’s big data companies eye IPOs as investors flush with cash
"A data model is only as good as the information used to create it and the requirements communicated by the business to the data engineers. Ideally, a data model brings together all the relevant data and relates tables from different data sources to one another so they can be queried by analysts in their entirety and in relation to one another."
Eva Murray - TDWI - How to Strengthen the Pillars of Data Analytics for Better Results
"Marketing teams must verify the quality of data before making it the bedrock of their strategy. They must understand that low-quality data creates low-quality insights."
Staff - Marketing Evolution - Using People, Processes, and Technology for Data Quality in Marketing
"There is lots of potential for data scientists to move in different directions [in the future], including marketing, communication and business development. Andreas suggested that the future controllers are likely to be data science-based. Data science will diffuse into many other professions, even unexpected areas like history – where text mining is omnipresent already."
Professor Andreas Brandenberg and Umberto Michelucci with Markus Grau - SAS - Which skills will dominate in the intelligent decisioning era?
"Where machines bring their strength, repeatability, and precision, humans can bring their flexibility, judgment, and dexterity. We can use both sets of qualities to build not just the same things better, but new things all the time."
Clara Vu - MIT Sloan - How data fuels the move to smart manufacturing
"CFOs have to lead by example to develop a data-driven culture: Decisions are to be based on actual facts and numbers, rather than doing things “because it’s how they’ve always been done” or based on gut feeling."
Michael Zimmel - B2C - 6 Ways Business Intelligence Makes Better CFOs
"Company leaders consume a vast amount of data and information, which can leave them feeling overwhelmed and paralyzed in decision-making."
Michael Davis - FMI - Strategies for Strengthening Resilience in a VUCA World