AI is the largest technology force of our time, with the most potential to transform industries. It will bring new intelligence to healthcare, education, automotive, retail and finance, creating trillions of dollars in a new AI economy.
As businesses look ahead to 2021 priorities, now's a great time to look back at where the world stands on global AI adoption.
Retailers like Walmart and Tesco are mining new AI opportunities for product forecasting, supply chain management, intelligent store installations and predicting consumer buying trends. Healthcare players in the age of COVID are trying to speed scientific research and vaccine development.
Meantime, educators are employing AI to train a data-savvy workforce. And legions of businesses are examining how AI can help them adapt to remote work and distance collaboration.
Yet mainstream adoption of AI continues to skew toward big tech companies, automotive and retail, which are attempting to scale across their organizations instead of investing in skunkwork projects, according to a 2019 McKinsey global survey of about 2,000 organizations.
We asked some of the top experts at NVIDIA where they see the next big things in AI happening as companies parse big data and look for new revenue opportunities. NVIDIA works with thousands of AI-focused startups, ISVs, hardware vendors and cloud companies, as well as companies and research organizations around the world. These broad collaborations offer a bird's eye view into what's happening and where.
Here's what our executives had to say:
CLEMENT FARABET
Vice President, NVIDIA AI Infrastructure
AI as a Compiler: As AI training algorithms get faster, more robust and with richer tooling, AI will become equivalent to a compiler - developers will organize their datasets as code, and use AI to compile them into models.The end state of this is a large ecosystem of tooling/platforms (just like today's tools for regular software) to enable more and more non-experts to program AIs. We're partially there, but I think the end state will look very different than where we are today - think compilation in seconds to minutes instead of days of training. And we'll have very efficient tools to organize data, like we do for code via git today.
AI as a Driver: AI will be assisting most vehicles to move around the physical world and continuously learning from their environments and co-pilots (human drivers) to improve, on their way to becoming fully independent drivers. The value of this is there today and will only grow larger. The end state is commoditized level 4 autonomous vehicles, relying on cheap enough sensor platforms.
BRYAN CATANZARO
Vice President, NVIDIA Applied Deep Learning Research
Conversational AI: Chabots might seem like so-last-decade when it comes to video games designed to take advantage of powerful PC graphics cards and CPUs in today's computers. AI for some time has been used to generate responsive, adaptive or intelligent behaviors primarily in non-player characters. Conversational AI will take gameplay further by allowing real-time interaction via voice to flesh out character-driven approaches. When your in-game enemies start to talk and think like you, watch out.
Multimodal Synthesis: Can a virtual actor win an Academy Award? Advances in multimodal synthesis - the AI-driven art of creating speech and facial expressions from data - will be able to create characters that look and sound as real as a Meryl Streep or Dwayne Johnson.
Remote Work: AI solutions will make working from home easier and more reliable (and perhaps more pleasant) through better videoconferencing, audio quality and auto-transcription capabilities.
ANIMA ANANDKUMAR
Director of ML Research, NVIDIA, and Bren Professor at Caltech
Embodied AI: The mind and body will start coming together. We will see greater adaptivity and agility in our robots as we train them to do more complex and diverse tasks.The role of high fidelity simulations is critical here to overcome the dearth of real data.
AI4Science: AI will continue to get integrated into scientific applications at scale. Traditional solvers and pipelines will be ultimately completely replaced with AI to achieve as high as a 1000x increase in speed. This will require combining deep learning with domain-specific knowledge and constraints.
ALISON LOWNDES
Artificial Intelligence, NVIDIA Developer Relations
Democratized AI: The more people who have access to the dataset, and who are trained in how to mine it, the more innovations that will emerge. Nations will begin to solidify AI strategies, while universities and colleges will work in partnership with private industry to create more end-user mobile applications and scientific breakthroughs.
Simulation AI: What does (insert AI persona here) think? The AI-based simulation increasingly will mimic human intelligence, with the ability to reason, problem solve and make decisions. You'll see increased use here for both AI research and design and engineering.
AI for Earth Observation (AI4EO): It may be a small world after all, but there's still a lot we don't know about Mother Earth. A global AI framework would process satellite data in orbit and on the ground for rapid, if not real-time, actionable knowledge. It could create new monitoring solutions, especially for climate change, disaster response and biodiversity loss.
KIMBERLY POWELL
Vice President & General Manager, NVIDIA Healthcare
Federated Learning: The clinical community will increase their use of federated learning approaches to build robust AI models across various institutions, geographies, patient demographics and medical scanners. The sensitivity and selectivity of these models are outperforming AI models built at a single institution, even when there is copious data to train with. As an added bonus, rese
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AI is the largest technology force of our time, with the most potential to transform industries. It will bring new intelligence to healthcare, education, automotive, retail and finance, creating trillions of dollars in a new AI economy.
