5 AI trends to watch in 2018 – O’Reilly Media
Ben Lorica, the Chief Data Scientist at O’Reilly Media, is looking at AI 5 trends to watch in 2018
- Expect substantial progress in machine learning methods, understanding, and pedagogy
- New developments and lowered costs in hardware will enable better data collection and faster deep learning
- Developer tools for AI and deep learning will continue to evolve
- We’ll see many more use cases for automation, particularly in the enterprise
- The AI community will continue to address concerns about privacy, ethics, and responsible AI
Artificial intelligence: The time to act is now – McKinsey&Company
Very insightful article by Gaurav Batra, Andrea Queirolo, and Nick Santhanam from McKinsey&Company, looking at the question if companies are prepared to capture value from the oncoming wave of innovation?
Well aware of AI’s massive potential, leading high-tech companies have taken early steps to win in this market. But the industry is still nascent and a clear recipe for success hasn’t emerged. So how can companies capture value and see a return on their huge AI investments? Our most important takeaway is that companies need to act quickly. Those that make big bets now and overhaul their traditional strategies will emerge as the winners.
To capture value in this growing market, companies are experimenting with different strategies, technologies, and opportunities, all of which require large investments. While much uncertainty still persists, companies that heed the following points will be better positioned to win.
- Value capture will initially be limited in the consumer sector
- Enterprise winners will focus on microverticals in promising industries – several industries now offer the strongest opportunities for AI: public sector, banking, retail, and automotive
- Companies must have end-to-end solutions to win in AI
- In the AI technology stack, most value will come from solutions or hardware
- Specific hardware architectures will be critical differentiators for both cloud and edge computing
- The market is taking off already—companies need to act now and reevaluate their existing strategies
And another really good article fromMcKinsey&Company
What AI can and can’t do (yet) for your business – McKinsey&Company
And another really good article by Michael Chui, James Manyika, and Mehdi Miremadi looking at AI’s limitations, helping executives better understand what may be holding back their AI efforts. They also highlight promising advances that are poised to address some of the limitations and create a new wave of opportunities.
AI’s challenges and limitations are creating a “moving target” problem for leaders: It is hard to reach a leading edge that’s always advancing. It is also disappointing when AI efforts run into real-world barriers, which can lessen the appetite for further investment or encourage a wait-and-see attitude, while others charge ahead
Business Leaders need to understand not just where AI can boost innovation, insight, and decision making; lead to revenue growth; and capture of efficiencies—but also where AI can’t yet provide value.
The 5 Things Your AI Unit Needs to Do – HBR
Alessandro Di Fiore, Simon Schneider and Elisa Farri from the European Center for Strategic Innovation (ECSI) have suggestions from their research and consulting experience with leading clients in Europe for managers who struggle to convert AI experiments into strategic programs.
They have identified five key roles that can help AI units to develop the right mission and scope of work to succeed.
- Scouting AI technology, applications, and partners.
- Experimenting with AI technology and applications.
- Supporting business units in applying AI technology.
- Getting the entire organization to understand AI.
- Attracting and retaining talent.
Winning the AI revolution isn’t about just the technology and the tools, it is about educating and getting your organization ready for the future. In the same way as Amazon didn’t invent the technology that has made them a corporate titan, companies in the AI-age need to prepare their organization to be data-first in order to stay competitive in the long run.
The Evolution of the AI/ML Application Space – yankeesabralimey
And last but not least a view from Gil Dibner, a global venture investor, on the evolution of the problem spaces to which AI/ML software is being applied – from native to easy to hard.
Who serves whom? – ia.net
Thought provoking essay looking at some of the ethical questions of AI.
It’s not only about business. AI is already shaping our everyday lives. And this is only the beginning. There are ethical questions like “Do machines serve us as much as we serve those who own them?” or “Should humans serve machines or should they serve us?” and “May we give machines the technical, legal and political power to make decisions in our place, subjecting us to their processes?”
We need to make sure now that we do not grow into a future where we cannot discern humane from artificial, fake from factual, where have no basis to decide what existence we want to lead. Right now, we need to make sure that the distinction between human and machine stays clear. We need to make sure that we and not those who own information technology decide what future we want. How?