The 7 most interesting things I’ve read over the past 7 days.
(image credit: Pixabay)
(1) The One Thing You Should Read about AI this week:
In March, we ran a TCS DEX event where we posed the question to our partners and clients, around whether every company should have an AI strategy. While there was general agreement about the need for an AI strategy, there was no clear starting point. This may be the challenge for most companies. And perhaps the first steps towards a strategy are gathering information and running experiments.
If you read one thing this week, read this AI paper by the McKinsey Global institute – they publish results from a comprehensive survey and analysis of AI across industries, functions, and use cases and by the relevance of the different techniques, such as Transfer Learning, Reinforcement Learning, and Deep Learning Neural Networks. If some of that sounds obscure to you, I suggest reading up a little bit as these will become common business parlance in the not-too-distant future, and clients will be asking about them. In any case, succinct explanations are provided in the paper. It will probably take you a couple of hours to read (not skim) the 40 odd pages. Here are some of the very high-level takeaways:
- Industries where the number of use cases are the highest, include (1) Insurance, (2) Banking, (3) Retail, and (4) Automotive & Assembly.
- Functions with the highest number of use cases include (1) Supply Chain management and manufacturing, and (2) Marketing and Sales
- Specific domains where the impact might be the highest include (1) customer service & management (2) risk modelling (3) predictive service / intervention (4) workforce productivity and efficiency (5) analytics-driven hiring and retention, and (6) yield optimisation.
Some other takeaways:
- The highest absolute impact of AI is to be found in Retail, but Travel and transport & logistics can extract the highest incremental value over other analytics techniques.
- Image data is the highest value, after structured and time series data, and ahead of text.
- Challenges and limitations: (1) labelling training data (2) obtaining large enough data sets (3) explaining the outcomes and decisions in clear enough terms – e.g. for product classification or regulatory (4) transferring of findings to adjacent use cases, and (5) risk of bias in data/ algorithms
(2) Data: Facebook is a misguided amateur compared to Palantir
Palantir is much more dangerous than FB. Why? (1) Because Peter Thiel, the founder is a man of metamorphosis – he has quixotic views of the world – such as ‘freedom is not compatible with democracy’; (2) because Palantir is a much more shadowy and secretive organisation but built specifically for next-generation analytics for powerful clients. (3) Because this kind of analytics power can be destructive if individuals go rogue – the article talks about Peter Caviccia who ended up running his own spying operation within JPMorgan in what is described in the article as Wall Street meets Apocalypse Now, and (4) because tools like this are being used by police forces such as LAPD to predict crime – but also to do that to build deep and intricate views of a lot of individuals and their lives. The article also provides a very good visual model of Peter Thiel’s incredible original Paypal team and network which includes Elon Musk, Reid Hoffman (LinkedIn), Steve Chen (Youtube) and many others.
(3) Design: Welcome to Generative 3D Design
What do you do when you need to design and build a spinal implant that needs to be appropriately strong, light and pliant? You use an algorithm-driven design process called generative design with 3D printing. Algorithmic design takes in your specifications or requirements and generates a number of options, which are developed faster than humans and enables a lot more personalisation of complex materials. In future, these will probably be custom built to specs in a way that humans simply can’t. It also uses the least amount of material possible (it’s one of the constraints/ objectives). This story in the Wired magazine talks about how Nuvasive does this using AI and 3D.
(4) eCommerce and Retail – change of guard, and disruption for the economy
This week we had a direct comparison between M&S vs ASOS: M&S is a struggling brand – losing share in apparel, and under pressure on foods. Other brick and mortar retailers like New Look are also in trouble. ASOS sales, on the other hand, hit £1.9 bn 2017 which amounted to a 33% increase. It’s also instructive to note that eCommerce contributes some 25% of British clothes retail numbers. In fact, the UK has the highest amount of online commerce (as a % of overall retail numbers – almost 18%), but the retail industry also accounts for 10% people and 10% of the economy – so significant disruptions lie ahead.
(5) Asset-light business models
We’ve seen them in telecoms (MVNOs), in retail, and also in utilities. Lightweight, direct to consumer competitors who don’t carry the baggage of their larger competitors. They have no legacy IT and are built ground up on digital platforms, for a start, and also have a much more nimble operating model. Companies like Asos and Ovo energy are successful because they attract a particular consumer niche, operate in an agile way and are not weighed down by the legacy business and IT challenges of their larger peers (zero inventory, for example). This trend goes all the way down to micro brands in the consumer goods space. Many of these businesses will die or stay micro, but once in a generation, they will lead to the next FB or Amazon.
(6) Alexa Fashion – a glimpse of the future
What’s Alexa’s next trick? How about a camera that can give you fashion feedback? Amazon’s Echo Look (not yet launched to the public, but on invitation only basis) has a camera and lets you take selfies and gives you feedback on what you’re wearing. For those worried about whether Amazon was listening to all your conversations, this will definitely be a step too far! This piece is a good take on the social and psychological implication of a tool like this. Of course, if you want algorithmic advice but don’t want something that invasive, you can always turn to Miquela
(7) Battery Wars
We all know that a move to electric cars is a ‘when’ and not an ‘if’ question by now. What that means, however, is a near insatiable demand for batteries and a huge spotlight on battery technology. Currently, the minerals that go into batteries such as Lithium and Magnesium are seeing a huge spurt in demand. It turns out that DR Congo is the worlds dominant source of Magnesium. In all of this, the UK is seeking to play a leadership role in battery technology. But is it either feasible or desirable? On the other hand, Williams has been working on safer batteries which are tough-tested in the Formula E competition – where electric-only cars race, collide and crash.