There are three basic approaches to AI: Case-based, rule-based, and connectionist reasoning.
We really believe that long-term, the way AI will drive is similar to the way humans drive – we don’t break the problem down into objects and vision and localization and planning. But how long it will take us to get there is questionable.
Not only can AI improve civilian safety, it will vastly improve battlefield logistics, creating better scenarios for men and women in uniform.
We’re at a point now where we’ve built AI tools to detect when terrorists are trying to spread content, and 99 percent of the terrorist content that we take down, our systems flag before any human sees them or flags them for us.
OpenAI is doing important work by releasing tools which promote AI to be developed in the open. Compute power is largely produced by NVIDIA and Intel and still relatively expensive but openly purchasable. Blockchains may be the key final ingredient by providing massive pools of open training data.
Understanding of natural language is what sometimes is called ‘AI complete,’ meaning if you can really do that, you can probably solve artificial intelligence.
We see incredible opportunity to solve some of the biggest social challenges we have by combining high-performance computing and AI – such as climate change and more.
I think that solving the job impact of AI will require significant private and public efforts. And I think that many people actually underestimate the impact of AI on jobs. Having said that, I think that if we work on it and provide the skill training needed, then there will be many new jobs created.
Now the playbook is we build AI tools to go find these fake accounts, find coordinated networks of inauthentic activity, and take them down; we make it much harder for anyone to advertise in ways that they shouldn’t be.
There are two companies that the AI Fund has invested in – Woebot and Landing AI – and the AI Fund has a number of internal teams working on new projects. We usually bring in people as employees, work with them to turn ideas into startups, then have the entrepreneurs go into the startup as founders.
We believe that one day Emotion AI will be ubiquitous, embedded on chips in our devices, ingrained into technology we use every day at home and at work.
Humans have 3 percent human error, and a lot of companies can’t afford to be wrong 3 percent of the time anymore, so we close that 3 percent gap with some of the technologies. The AI we’ve developed doesn’t make mistakes.
I am often asked what the future holds for Emotion AI, and my answer is simple: it will be ubiquitous, engrained in the technologies we use every day, running in the background, making our tech interactions more personalized, relevant, authentic and interactive.
Emotion AI will be ingrained in the technologies we use every day, running in the background, making our tech interactions more personalized, relevant, authentic, and interactive.
Many researchers are exploring other forms of AI, some of which have proved useful in limited contexts; there may well be a breakthrough that makes higher levels of intelligence possible, but there is still no clear path yet to this goal.
Beyond helping other people build AI systems with Deeplearning.ai, I also hope to build some AI systems myself!
The tools and technologies we’ve developed are really the first few drops of water in the vast ocean of what AI can do.
AI cloud is just very, very nascent.
As we continue to apply AI to new fields, ethical dilemmas will arise and the answers will not be clearly defined.
AI is everywhere. It’s not that big, scary thing in the future. AI is here with us.
The real problem is not the existential threat of AI. Instead, it is in the development of ethical AI systems.
Silicon Valley and Beijing are the leading hubs of AI, followed by the U.K. and Canada. I am seeing a lot of excitement in India, going by the number of people who are taking Coursera courses on AI.
Even a cat has things it can do that AI cannot.
