Hi! I am back again with the transcript of a new episode of Capgemini’s “Data and The Hunch” podcast series. I am a Principal Analyst for VINT, the Sogeti Trend Lab, and work on anything Analytics related to the Connected Service Experience. My Twitter handle is @BLO2M – B-L-O-Numeric2-M – so if you like, feel free to follow me. Here’s the transcript:
Today, Fenny has joined me in the studio. Hi Fen, how can I help you?
Well Jaap, I’m here to fire some questions. Now first, you talk about Machine Intelligence – but where does that leave AI, Artificial Intelligence?
Right! Well, mainly in business and IT environments, there is a growing tendency of abandoning the 60-year old notion of Artificial Intelligence (AI) – all the more since it has become feasible to deploy practical machine learning in the cloud, on offer from respectable vendors like Microsoft and Amazon on a pay-as-you-go basis.
Cloud-based machine learning – and deep learning definitely will be next – can now be attached seamlessly as a sophisticated and practical productivity accelerator to your Business Intelligence and Business Analytics practice (BI and BA), and also to Big Data efforts.
But we’re not done with AI, on the contrary. At the same time, so-called Integrative AI is on the rise: vision, speech, natural language, machine learning and planning are brought together to create systems, capable of seeing, understanding, and having meaningful conversations with people. Now the question isn’t “to AI or not to AI,” but how to do both – machine learning, deep learning and Integrative AI – everything in a practical and impactful fashion.
Sounds cool, but where does it leave notions like Narrow AI and Super AI? Where do they fit in?
Good question, thank you! Now, since the field is developing at a frantic pace, and already hosts numerous successful startups with fancy names like Numenta, MetaMind, AI.ONE, WIT.AI, Clarifai, Jibo, and Palantir – after the seeing stones in Lord of the Rings – it is even more important to pay attention to the official main AI domains of Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI).
I won’t go into any detail here but new advances and viewpoints today literally beg for the integration of Natural and Artificial Intelligence, and also of Human and Machine Intelligence or MI. Numenta already touts “the new era of Machine Intelligence.” MI recently was even proposed by BloombergBETA investor Shivon Zilis to replace AI as the new overarching notion. But, more importantly, instead of intelligence, we’d better simply talk about specific faculties or competencies, for that’s what it all comes down to!
I’m sure people are wondering how all this new intelligence stuff relates to daily business technology practice . . .
Sure! On the administrative business side, we’ve been witnessing ever more informed and intelligent decision making, starting with Relational Database Management Systems (RDBMS), Data Warehouses (DW), and online analytical processing (OLAP) – blending over into Business Intelligence (BI), which is a matter of sense & respond), into Business Analytics (BA), a matter of anticipate & shape, and finally into Big Data, Predictive Analytics, Machine Learning, and Deep Learning. That’s the line of development for many organizations in further optimizing their value chain.
Okay, so if I understand you properly, you mean there are two seemingly competing strands while new horizons open up: AI on the one hand and MI on the other. Now, what would you consider the main takeaways?
Indeed Fenny – there’s no real difference between AI and MI. It’s all mainly a matter of taste as former well-defined subfields are integrating and expanding. Overseeing all this, there are three main messages. First, we should carefully distinguish between the two ends of the AI/MI spectrum, and make sure to not mix them up – that is the fundamental one and the practical one. Second, the most sensational developments are definitely more on the AI side, where advanced robotics, self-driving cars, brain-like processors, and wetware rank among the achievements that tend to wipe out the sci-fi category within our decade. Now, this pace of change is the third and probably the most important message!
But what are the chances, Jaap, of this being yet another technology bubble?
Breakthrough innovations on the AI side already have started to bear relevance for vertical and horizontal business domains, to the effect of heavy investment and startup activity. Partly, this might be yet another AI or MI bubble but at least the impact of practical machine learning, algorithms, and deep learning on informed decision making continues to reshape the data first mindset and analytics culture in organizations. It means that practical AI and MI techniques are a proven lever for lifting the bottom line of your Corporate IQ.
This should be evangelized, guided and monitored from the top of the organization. Bill Gates, the guy who from his point of view firmly believes that a breakthrough in machine learning is worth ten Microsofts, also states that “the CEO’s role in raising a company’s Corporate IQ is to establish an atmosphere that promotes knowledge sharing and collaboration.” Corporate IQ today implies a data first, data science attitude – and that means: throughout the whole organization.
Sounds great Jaap, apparently there’s a lot going on on the practical side!
Most certainly! Recently, I met Carlos Guestrin. He is the Amazon Professor of Machine Learning at the University of Washington, and also the founder and CEO of Dato, formerly GraphLab. Dato delivers ultra-fast data analytics, best-in-class predictive modeling, and production-ready data science. In this way, Mr. Guestrin’s Dato is taking the next step. The company’s “mission is to accelerate the creation of intelligent applications by making sophisticated machine learning as easy as ‘Hello World’!”
Carlos Guestrin points at posterchilds like Amazon, Google, Netflix, Pandora, Uber, Fitbit, LinkedIn and a few others, which he calls: “disruptive companies differentiated by intelligent applications using machine learning.” Now, with practical cloud-based machine learning, every company today already can organize their own level of disruption, or fend off competition.
My final advice: the pace of change is furious, so stay up to speed and prepare for driving in the fast lane by supercharging your company culture of data analytics with artificial and/or machine intelligence.
Thank you Jaap; see you next time!
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