An AI pioneer reflects on how companies can use machine learning to transform their operations and solve critical problems.
Andrew Ng has worn many hats in his life. You may know him as the founder of the Google Brain team or the former chief scientist at Baidu. You may also know him as your own instructor. He has taught countless students, curious listeners, and business leaders about the principles of machine learning through his wildly popular online courses.
Now in his latest venture, Landing AI, which he started in 2017, he is exploring how businesses without giant data sets to draw on can still join in the AI revolution.
On March 23, Ng joined MIT Technology Review’s virtual EmTech Digital, our annual AI event, to share the lessons he’s learned. Read More
Tag Archives: Strategy
Is AI Adoption Going Way Too Fast?
The COVID-19 pandemic has accelerated the pace of AI adoption, but many industry insiders find the speed of adoption a bit overwhelming, according to a KPMG survey.
The KPMG report, based on a survey of 950 full-time business/IT decision-makers with at least a moderate amount of AI knowledge working at companies with over $1 billion in revenue, analysed the uptake, concerns, and confidence in AI across seven industries – tech, government, retail, financial services, industrial manufacturing, healthcare & life sciences.
According to Traci Gusher, Principal of AI at KPMG, industries are experiencing a COVID-19’ whiplash’ with AI adoption skyrocketing due to the pandemic. Meanwhile, experts have reposed faith in AI’s ability to solve significant business challenges. Read More
The great data decentralization is coming — are you ready?
The move to cloud computing is one of the most important technology shifts of our generation. Along with it, the decades-long push to centralize data storage in a single warehouse is coming to an end, as dumping everything into a “data lake” has caused more harm than good.
For some applications, centralizing data via cloud storage solutions such as Amazon S3 and Snowflake works to an extent (read: Snowflake’s IPO). At the same time, several major factors are creating greater data decentralization. Here are three of the biggest. Read More
Top Artificial Intelligence Influencers To Follow
This is a live list of top trending artificial intelligence experts/influencers from around the world. This list is last updated on March 8, 2021. This post will be updated regularly to reflect any new updates in the list. If we missed any name in the list, you can nominate a profile via email asif@marktechpost.com.
Here is our list updated as on March 8, 2021. Read More
AI adoption accelerated during the pandemic but many say it’s moving too fast: KPMG survey
Outlook for AI Optimistic Under the Biden Administration; More Want AI Regulation
The COVID-19 pandemic has accelerated the pace of artificial intelligence (AI) adoption, but many say it’s moving too fast, according to a new KPMG survey. Despite concerns about the speed of adoption, business leaders are confident AI can help solve some of today’s toughest challenges, including COVID-19 tracking and vaccines.
In the new study, Thriving in an AI World, high numbers of business leaders from the following industries say AI is at least moderately functional in their organizations, including those in: industrial manufacturing (93 percent), financial services (84 percent), tech (83 percent), retail (81 percent); life sciences (77 percent), healthcare (67 percent) and government (61 percent). In addition, several industries saw a significant increase from last year’s report: financial services (37-percentage point increase), retail sector (29-percentage point increase) and tech sector (20-percentage point increase). Read More
13 Common Mistakes That Can Derail Your AI Initiatives
13 experts from Forbes Technology Council share common mistakes to watch out for when implementing AI.
- Adopting Too Many Tools At Once
- Not Having A Clear Objective
- Not Having A Single Source Of Truth
- Not Analyzing Enough Data
- Incorrectly Structuring Datasets
- Implementing Siloed Solutions
- Not Having The Right Size Team
- Not Doing The Necessary Groundwork
- Assuming AI Is A Catch-All Solution
- Misidentifying Both The Problem And The Best Solution
- Implementing AI For Its Own Sake
- Implementing Solutions Without Sufficient Data
- Thinking AI Is ‘One-Size-Fits-All’
3 ways CIOs can use artificial intelligence (AI) to grow business in 2021
Amid the growth of the Artificial Intelligence (AI) and big data market, business leaders are starting to realize that AI – like any other business function – requires structured strategy, planning, training, and execution to successfully implement.
Many companies working on digital transformation have amassed huge data archives but lack the ability to extract the information they need to unlock new synergies and growth paths. This bottleneck is visible in most companies I meet. The transition from data collection to fully formed, AI-driven growth strategy is a multi-step process that can appear overwhelming to those without clear guidance. Read More
Towards Broad AI & The Edge in 2021
There are those who debate whether the new decade of the 2020s commenced on 1 Jan 2020 or 1 Jan 2021. Either way, one suspects that many around the world will hope that at some point during the course of 2021 the current year will mark a shift away from the events of 2020 and allow for a new start. For a definition of AI, Machine Learning and Deep Learning see the Article an Intro to AI.
A new administration is in place in the US and the talk is about a major push for Green Technology and the need to stimulate next generation infrastructure including AI and 5G to generate economic recovery with David Knight forecasting that 5G has the potential – the potential – to drive GDP growth of 40% or more by 2030. The Biden administration has stated that it will boost spending in emerging technologies that includes AI and 5G to $300Bn over a four year period. Read More
AI: The Horsepower of the Future
The year 2021 may well see a turn in the trajectory of AI. As DataRobot notes in its predictions for the new year, “Within the enterprise, we finally expect a wholesale move from ‘Experimental AI’ to ‘Operational AI,’ as organizations must move out of the lab and beyond pure experimentation.” In fact, Gartner is forecasting that 75% of enterprises will shift from piloting to operationalizing AI by the end of 2024, driving a 5x increase in streaming data and analytics infrastructures.
From our perusal of the publications offering predictions in this space we see agreement that AI will be a bigger disruptor to business than the Internet was. Again, we ask: why? The simple answer may be that it offers such a wealth of opportunities to make better decisions, unearth hidden relationships previously unnoticed among critical data, and offer the agility and automation that our speed-obsessed times demand for competitiveness. AI is the new horsepower. Read More
National AI strategies – A summary of major initiatives
AI is central to the future competitiveness of nations. Here is a summary of the major initiatives from nations who have declared a national AI strategy. I have listed the original source at the end of the blog. I have added some links in the case of each nation which I found interesting. Read More
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