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Artificial Intelligence podcast (AI podcast) is a series of conversations about technology, science, and the human condition hosted by Lex Fridman. Here are some next steps:

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(Updated via script on Feb 26, 2020)

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Jack Dorsey

CEO, Square & Twitter

Dmitry Korkin

Computational Biologist, WPI

Stephen Wolfram

Computer Scientist, Physicist

Eric Weinstein

Mathematician

Richard Dawkins

Evolutionary Biologist

David Silver

Research Lead, DeepMind

Roger Penrose

Physicist, Oxford

William MacAskill

Philosopher, Oxford

Nick Bostrom

Philosopher, Oxford

Simon Sinek

Author & motivational speaker

Anca Dragan

Roboticist, Berkeley

Vitalik Buterin

Creator of Ethereum

Lee Smolin

Theoretical Physicist

Ann Druyan

Creator of Cosmos

Alex Garland

Writer, Director

John Hopfield

Physics, Neurobiology (Princeton)

Marcus Hutter

Senior Scientist, DeepMind

Michael I. Jordan

Machine Learning, Berkeley

Andrew Ng

Educator, Researcher, Leader in AI

Scott Aaronson

Computer Scientist, UT Austin

Vladimir Vapnik

Statistician, Columbia U.

Jim Keller

Senior VP, Intel

David Chalmers

Philosopher, NYU

Cristos Goodrow

VP Engineering, Google/YouTube

Ayanna Howard

Roboticist, Georgia Tech

Daniel Kahneman

Psychologist and Economist

Grant Sanderson

Math Educator

Stephen Kotkin

Historian, Princeton

Donald Knuth

Computer Scientist

Melanie Mitchell

Professor, Portland State U.

Jim Gates

Professor, Brown

Sebastian Thrun

Kitty Hawk, Udacity, Waymo

Rohit Prasad

Head Scientist, Amazon Alexa

Judea Pearl

Professor, UCLA

Ray Dalio

Founder, Bridgewater Associates

Noam Chomsky

Professor, MIT

Gilbert Strang

Professor, MIT

Dava Newman

Professor, MIT

Michael Kearns

Professor, UPenn

Elon Musk

Tesla, SpaceX, Neuralink

Bjarne Stroustrup

Creator of C++

Sean Carroll

Theoretical Physicist, Caltech

Garry Kasparov

Chess World Champion

Michio Kaku

Theoretical Physicist, CCNY

David Ferrucci

Former Lead, IBM Watson

Gary Marcus

Professor, NYU

Peter Norvig

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Artificial Intelligence Podcast | AI Podcast | Lex Fridman

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The business worlds enthusiasm for artificial intelligence has been building towards a fever pitch in the past few years, but those feelings could get a bit more complicated in 2020.

Despite investment, research publications and job demand in the field continuing to grow through 2019, technologists are starting to come to terms with potential limitations in what AI can realistically achieve. Meanwhile, a growing movement is grappling with its ethics and social implications, and widespread business adoption remains stubbornly low.

As a result, companies and organizations are increasingly pushing tools that commoditize existing predictive and image recognition machine learning, making the tech easier to explain and use for non-coders. Emerging breakthroughs, like the ability to create synthetic data and open-source language processors that require less training than ever, are aiding these efforts.

At the same time, the use of AI for nefarious ends like deepfakes and the mass-production of spam are still in their earliest theoretical stages, and troubling reports indicate such dystopia may become more real in 2020.

Here are six predictions for the tech in this new year:

A high-profile research org called OpenAI grabbed headlines in early 2019 when it proclaimed its latest news-copy generating machine learning software, GPT-2, was too dangerous to publicly release in full. Researchers worried the passably realistic-sounding text generated by GPT-2 would be used for the mass-generation of fake news.

GPT-2 is the most sophisticated of a new type of language generation. It involves a base program trained on a massive dataset. In GPT-2s case, it trains on more than 8 million websites to understand the general mechanics of how language works. That foundational system can then be trained on a relatively smaller, more specific dataset to mimic a certain style for uses like predictive text, chatbots or even creative writing aids.

OpenAI ended up publishing the full version of the model in November. It called attention to the excitingif sometimes unsettlingpotential of a growing trend in a subfield of AI called natural language processing, the ability to parse and produce natural-sounding human language.

The resource and accessibility breakthrough is analogous to a similar milestone in the subfield of computer vision around 2012, one widely credited with spawning the surge in image and facial recognition AI of the last few years. Some researchers think natural language tech is rumored to be poised for a similar boom in the next year or so. Its now starting to emerge, Tsung-Hsien Wen, chief technology officer at a chatbot startup called PolyAI, said of this possibility.

Ask any data scientist or company toiling over a nascent AI strategy what their biggest headache is and the answer will likely involve data. Machine learning systems perform only as well as the data on which theyre trained, and the scale at which they require it is massive.

One reprieve from this insatiable need may come from an unexpected place: an emergent new machine learning model currently best known for its role in deepfakes and AI-generated art. Patent applications indicate that brands explored all kinds of uses for this tech, known as a generative adversarial network (GAN), in 2019. But one of its unsung, yet potentially most impactful, talents is its ability to pad out a dataset with mass-produced fake data thats similar but slightly varied from the original material.

What happens here is that you try to complement a set of data with another kind of data that may not be exactly what youve observedthat could be made upbut that are trustworthy enough to be used in a machine learning environment, said Gartner analyst Erick Brethenoux.

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Today, business innovation andartificial intelligence(AI) seem to go hand-in-hand to improve many areas of a business. In customer service, for example, chatbots are now interacting with online customers to improve customer service. In HR, artificial intelligence is being used to speed up the recruitment process, and inmarketing, AI-powered tools are increasingly used to personalize the customer experience.

A 2018studyby McKinsey Global Institute predicts that by 2030, 70 percent of companies might have adopted at least one type of AI technology. (The study was based on two independent surveys of 1,600 business executives across industries worldwide and 3,000 corporations in 14 sectors in 10 countries.)

Despite common fears surrounding the impact of artificial intelligence on employment, AI is here to stay. AI and machine learning (a subset of AI) are at the forefront of business innovation.

Let's take marketing as an example: To get some insights into how artificial intelligence can help you take your marketing strategy to the next level, I spoke toNeal Schaffer, a leading social media marketing strategist.

(Schaffer is the author ofthree bookson social media, with his fourth book,The Age of Influence, set to be published in 2020.)

According to Schaffer, artificial intelligence isn't changing the approach to marketingyet.

"I think we're seeing, in the different tools that exist for marketers, that slowly these tools are beginning to leverage AI and machine learning to give [them] better insight into their marketing activities."

In this day and age, Schaffer adds, everyone is awash in data. Using these tools can help marketers analyze the data swirling all around them.

"I think AI is actually going to save marketers a lot of time and also make them look smarter," he says.

AI has found its way to influencer marketing, as well.

"How do you determine who is an influencer when there are so many fake followers and so much fake engagement that exists on the Internet?" Schaffer says. "We can teach the AI engine how to spot people when they have an unnatural spike in follower increase in a limited amount of time, which could be a sign of fake followers.

"If they average 1 percent engagement," he continues, "but all of a sudden have 5 percent engagement when they publish a sponsored post using #ad, we might suspect that they participated in an Instagram pod to boost engagement levels. These are some of the ways AI can automatically flag accounts that require further investigation when their influence should be questioned. We can teach artificial intelligence to look for these trends that might indicate that these people are faketheir influence is not authentic."

Artificial intelligence isn't just for huge companiessmall or medium-size business can also harness the power of AI to maximize the ROI of their marketing efforts. Achatbotthat interacts directly with users on your website is a great entry point for "companies to first really experience what AI can do for their business," Schaffer says.

Schaffer uses chatbot technology customized for his own use-case scenario on his website. Chatbots can increase engagement with a website visitor, Schaffer explains.

"People come to your website and not everyone engages. Not everyone goes to a contact form or buys a product," he says. "But a lot of people are very comfortable engaging with these bots these days. So I'm getting more people engaging with my bot and having a conversation with me there versus going all the way to my contact form and filling out that form."

There are a number of tools available right now that leverage AI and are accessible to smaller businesses. For example, there are tools that use AI and natural language processing to understand a company's keywords and brand messaging, and basically repurpose a company's content.

Adam Geitgey, software engineer and consultant

If you have a blog post, you can repurpose your content into a number of tweets or LinkedIn or Facebook posts, Schaffer says. This is intelligently using AI to provide, "bite-sized content that you can then use for your social media messaging or even your newsletter messaging," he explains. "So this is another very, very easy way to experience AI."

Machine learning is a key part of business innovation today, according to software engineer and machine-learning expertAdam Geitgey. Geitgey teaches software engineers how to build machine learning systems and is the author ofMachine Learning Is Fun.

"Machine learning," says Geitgey, "is very effective when you have a repetitive process that you want to automate, but the process is a little too complicated to automate with traditional programming."

Geitgey explained this with an example:

"Imagine that you are building an online business where users can upload their photos to your website or app. With user-generated content, you might want to make sure that your users aren't uploading illegal or inappropriate pictures. It would be too time-consuming to look at every photo yourself to pick out the bad apples, but machine learning is great at this. You can use a machine learning system to instantly scan each image and flag the ones that are most likely breaking the rules. That way, you only need to double-check a few flagged images instead of checking them all."

"Any place like this in your business," continues Geitgey, "where you need to sort throughlots of dataand make a quick decision about each piece of data is a place where machine learning might save you lots of time and money. So look for these kinds of opportunities."

Machine learning can also be used for transcribing audio to text, recognizing objects in photographs or videos or automatically understanding if a user is writing something negative or positive in a comment.

Embarking on an AI journey naturally raises concerns for any business. What are some mistakes that a company can avoid?

"The most common mistake I see," Geitgey says, "is when businesses feel pressure to 'get some AI' before they understand what it can actually do. It's the old problem of a solution looking for a problem. Machine learning and AI can be hugely beneficial, but it isn't magic, and it isn't always a good fit. Imagine a machine learning system as an employee who is really slow to learn, but once they know how to do something, they can do it forever without getting tired."

When working with a person, you only have to show them a few examples and give them the basic rules before they get it. But with machine learning, the system needs "hundreds of thousands of examples " before it understands, Geitgey explains.

"Because of this, machine learning is only going to work well for certain kinds of problems where you have lots of examples to work with," he says. "So be strategic! Start with the problems you most need to solve in your business, and then work backward and see if machine learning might be a good solution for them. Don't start by assuming that you need to use machine learning and AI just because everyone else is using it."

Should a company build a custom solution or use an off-the-shelf solution?

"Read up on what out-of-the-box machine learning systems from popular vendors can do and what requires building a new machine learning system from scratch. Many vendors already have great solutions for problems that lots of businesses face, like transcribing audio, classifying images and reading words out of images. If that's what you need, you can use an off-the-shelf solution quickly and easily.

"But if you are trying to automate a process that is very specific to your business and that depends on data that only you have," he adds, "you'll probably have to build a custom machine learning system. That takes more time and costs more money."

With AI poised to take over an ever-increasing number of industries, it helps to stay-up-to-date on this exciting emerging technology so that you can leverage it for your business. Business innovation is all about introducing new ideas into your business. AI is a new idea worth considering.

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Photo: Getty Images, Neal Schaffer, and Adam Geitgey

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Business Innovation: How Artificial Intelligence Is ...

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There are myriad articles on artificial intelligence and its application in business. As AI continues to grow and permeate seemingly every aspect of business, its important to cut through the noise and focus on where AI fits in your organization and how to best implement it.

I'm the founder and CEO of an AI-based customer relationship management platform. Through this experience, I've learned a few ways leaders can determine their own approach to AI.

A Brief Overview Of AI Application In B2B

Broadly speaking, AI is a branch of computer science concerned with replicating human intelligence in machines. Depending on whether you run a business-to-consumer or business-to-business company, you might find some types of AI more relevant to your business than others.

In B2B, AI is all about data and analysis to make better-informed decisions. For example, if you have enough sales and customer data, you can use predictive analytics to figure out your ideal customer profile and/or potential customer base and adjust your marketing strategy and campaigns accordingly.

In more technical terms, AI applications in B2B can be broken into three types of machine learning: supervised, unsupervised and reinforcement.

In the case of supervised learning, you or someone with business intelligence skills feeds the data to the learning algorithm (a statistics algorithm) and sets a goal (what you want to get to or what youre looking for). The machine then tries to match that goal.

In unsupervised learning, the algorithm looks at the data and searches for patterns. As the name suggests, there are no instructions given prior to the analysis. For example, it can look at your customer data and decide that you have a cluster of customers in the manufacturing industry that looks really promising.

Finally, in reinforcement learning, which is more advanced, the algorithm looks at the data and comes up with a set of conclusions. You dont provide a predefined dataset or any guidance; its more of a trial-and-error method. You look at the results and tell it whether the conclusions are correct, and it continues to reinforce the right steps to get to an endpoint.

How can AI benefit your business?

For businesses that collect a lot of customer data at every point, being able to use AI to derive meaning from that data can help get ahead of the competition. You can spot trends early and identify areas where you're losing revenue or where you could potentially gain revenue. You can then make data-driven decisions and quickly adapt to changes.

AI can also impact your CRM system and team productivity by helping identify leads, building effective nurture campaigns or personalizing the customer experience. (A number of companies, my own included, offer CRM and marketing AI solutions.)

Although theres some concern about AI replacing jobs, I believe there's an opportunity for AI to help, not hinder, the performance of marketers, salespeople and customer service representatives. However, taking steps to introduce it successfully is critical.

How can you introduce AI in your business?

Make sure you are clear on where in the business you want to use AI and what you hope it will solve for you. Keep in mind that you need to have enough data to make your AI investment worth it. Once youve done that, train your employees on how itll work. Remember: Its not a black box.

When introducing any new technology, its always good to begin with a small project and work from there. Start with a hypothesis and a goal, and at the end, analyze how well you did and if you reached the right conclusions. The first project is really about the journey more than the end goal.

Finally, consider any challenges that might come your way. For example, there are two sides to managing AI expectations. Some people on your team might think its awesome and will solve a lot of problems. Others might get scared, thinking its going to replace their jobs.

Try to address the expectations and concerns of both extremes. AI is not going to solve everything, and in a B2B company, it most likely wont replace jobs. You have to tamp down both the enthusiasm and worries surrounding AI to ensure buy-in before you make it part of your business.

What technology do you need to implement AI for the first time?

You can start by using available cloud computing resources, which can be helpful for small to midsized companies because you dont need to know a lot of underlying methodologies.

Alternatively, you might decide to set up AI technology on-premises. If you go this route, keep in mind that youll need some hefty horsepower and someone with a lot of knowledge of the underlying analytical algorithms and statistics to run through big datasets and get the highest ROI from AI.

Increasingly, I believe its not a question of if, but when you should implement AI in your business. The sooner you figure out your AI approach, the sooner youll start reaping its benefits.

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Tips On Introducing Artificial Intelligence In Your Business - Forbes

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The AI sector is booming. Thanks to several leaps that have been made, we are closer than ever before to developing an AI that acts and reacts as a real human would do. Opportunities in this sector are flourishing, and there is always a way for you to get involved.

Photo by Annie Spratt.

Employees: If you are searching for a job in the tech sector, one of the most rewarding you could find is working with AI. It is a mistake to assume that all AI development is focussed on developing android technologies. There are many other applications for AI and each one needs experts at the helm to help bring it to fruition.

Whether you are a graduate, or you are looking for a change in careers, there is always a job opening that you could look into. Even if you dont have a background in this tech, there are many other ways you could get involved, whether you are working on an AIs cognitive abilities or even just testing out the product. Whatever your background and skillset might be, there is always a way for you to get involved.

Investors: AI development is incredibly costly. While many of the smaller developers may have a great idea that could be world-changing if they bring it to fruition. However, they often lack the finances to be able to do so. This is where investors can come in.

Investors like Tej Kohli, James Wise, or Jonathan Goodwin may have little expertise in these areas from their own personal experience, but they know how to recognise a viable idea when presented with one. Whether you are looking to get into venture investment yourself or you are a tech company looking for financial backing, their activities should give you some idea about the paths you need to follow.

Photo, Bence Boros.

Consumers: The world of AI isnt just open to investors and tech gurus. There is now a vast range of AI-driven tech emerging onto the market. You, as a consumer, get to be an instrumental part of driving this new tech forward as it means that the developers gain some insight into what features are popular and which arent.

Just look at the boom in home assistants that has erupted in the past few years. We are now able to live in fully functioning smart homes with music playing and lights turning off with a simple voice command. By exploring what AI has to offer through the role of the consumer, this all feeds back to the developers and helps them create the next generation of products.

No matter how interested you are in this sector, there is always going to be something you can pursue that will help to develop AI overall. This is an incredibly exciting era to live in, and AI is just one of the pieces of tech that could transform the world as we know it. Take a look at some of the roles and opportunities and see where you could jump in today.

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What Opportunities are Appearing Thanks to AI, Artificial Intelligence? - We Heart

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