“AI is the fastest-growing industry that I have seen in the last thirty years of my career “. This is one of the initial assertions of Paul Daugherty, Accenture’s Chief Technology and Innovation official.
We re-paid attention to this phenomenal discussion with have Azeem from the Exponential view podcast and acquired a lot of bits of knowledge into how research in AI will compel us to upgrade the world we live in.
Here’s our 7 key takeaways from the conversation:
1. AI is just an approximation of human abilities.
While many are talking about Generic Artificial Intelligence making humans irrelevant, Paul argues that AI is a set of capabilities covering three broad categories: sensing, comprehension and learning. Seen in this way, AI extends humans and allows them to do old things in new ways. AI for business offers tools for forward-thinking organizations.
2. AI releases us from the complicated logic of Computers
How many of us are still struggling on a daily basis with complicated Excel sheets or with out-of-date software difficult to use? Not anymore.. AI for business is liberating us from all this complication. By being “smarter” than traditional software, it is more flexible and more “human” in the interaction. More specifically, it allows us to draw usable insights from squiggly, ambiguous data like images, speech and natural language.
3. AI is the new Electricity..
Embracing AI might mean re-writing the very nature of your business, Azeem argues that AI transformation will be similar to the transition to electrified factories that took place in the 1900s.
Prior to electrification, factories were powered by large steam engines. These engines released energy in huge volumes and as a result were difficult to manage. As a consequence, tools were big and needed to be shared around the factory floors.
Electricity allowed for the flow of smaller quantities of power that could instead be distributed across different parts of the factory. Through electricity, factories were able to break down processes, modularise and speed-up production chains.
AI will have a similar transformational power to unbundle complex, monolithic processes and favour automation through a variety of different “smart” tools across organizations.
4. AI has arrived as a torrent.
AI has grown so fast in the enterprise world, for three main reasons: firstly, AI can solve customer problems faster than humans. Secondly, the data and infrastructure to implement AI is already there. Thirdly, access to AI is not democratised: all the discoveries in the areas of Machine Learning and Computer Vision are made accessible.
5. Data network effect ensures a competitive edge
Machine Learning needs data to learn models from. If your models are performing well in your products, customers will use them more. This will result in more data created by your customers that you can use to further improve your AI. This is what we call AI locking loop: A reinforcing effect, a winner-take-all approach that rewards the companies that start early with AI.
If there’s no data at the core of the DNA of your company, you will struggle in the medium and longer term to compete with the players leveraging the data network effect.
6. Use Machine Learning in every part of your business.
Amazon adopted this strategy early after the Deep Learning revolution in 2012. Jeff Bezos in one famous memo stated: ” we need to embed AI in everything we do”.
They started with a program that would train all their managers and senior execs on the capability of Machine Learning and AI. Moreover, in the business plans of every amazon program manager they introduced a mandatory item: how can I improve my part of the business through AI. What can AI do for business? Everything according to the big players like Amazon.
7. Next wave of AI business value is created by combining Symbolic AI and Machine Learning
For a lot of business applications, especially in verticals such as Manufacturing, Talent Management & HR, the name of the game is to take previous knowledge and rules devised by human experts into account. For this reason, Adam and Azeem argue that traditional AI methods like symbolic graph reasoning are regaining a “raison d’etre” and complement more modern learning techniques.
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