Researchers from Duo Security, an authentication services company owned by Cisco Systems, have published a blog post that explains how to methodically identify “amplification bots.” These are defined as automated Twitter accounts that purely exist to artificially amplify the reach of content through retweets and likes.

The article, “Anatomy of Twitter Bots: Amplification Bots,” was written by researchers Jordan Wright and Olabode Anise. It expands upon their talk at the 2018 Black Hat USA conference, “Don’t @ Me: Hunting Twitter Bots at Scale.”

The pair created a dataset of 576 million posts and filtered it to show those that had over 50 retweets and attempted to define what it considers to be normal behavior. Through their analysis, they found that found that half of the tweets have a 2:1 ratio of likes-to-retweets. Around 80 percent had at least more likes than retweets (greater than 1:1 ratio).

A tweet that’s likely to be artificially amplified will flip that on its head and have more retweets than likes. One example highlighted in the article had 6 retweets for every one like. The pair deems a tweet to be artificially inflated if it has a retweet-to-like ratio that’s greater than five.

 

 

The pair also argue that timing plays an important role in identifying phony accounts, with a genuine user’s tweets being in chronological order. A fake account, on the other hand, is more likely to take a more scattered approach to posting.

Using these clues, the researchers created a methodology to determine, with some degree of confidence, if an account is an amplification bot.

The first point is obvious: it retweets posts. A lot. If over 90 percent of an account’s posts are retweets, that’s a clue.

The next step is to analyse how many of these tweets are “amplified.” If at least half of them have a retweet-to-like ratio greater than 5:1, it’s a glaring clue.

 

 

The next step is to look at the timings of the tweets in order to count the number of “inversions,” or not in chronological order.

The pair claims to have identified over 7,000 amplification bots in just one day by using this methodology, but it’s entirely possible that this is the tip of the iceberg. In the paper, Wright and Anise explain that it’s impossible to identify accounts that amplify content through likes, as there’s no official Twitter API endpoint for capturing and recording likes.

Regardless, this is a problem for both security researchers and Twitter to tackle. Amplification bots sound harmless, but as we learned in 2016, can be used by a foreign adversary to shape public opinion. Retweets, as the authors of the blog post explain, don’t just affect how content spreads, but also its perceived credibility.

Amplification bots can also be used in influencer fraud, which is believed to cost the marketing industry $100 million annually.

Wright and Anise previously wrote about how to use similar data science techniques to identify fake followers on Twitter. To read about that, click here.

#Insights #Twitter #InternetBot #DataScience

 

AI World speakers described bringing human traits, ethics, and lots more data to machine learning applications.

BOSTON — Cold weather this week didn’t matter to the crowds at the AI World conference here, as activity around artificial intelligence continues to heat up. Over three days, more than 2,200 attendees learned about the latest advances in machine learning, deep learning, and the industries being affected by AI.

While most of the conference focused on AI’s impact on the healthcare, pharmaceutical, and enterprise software markets, a few sessions discussed industrial automation efforts, including the Industrial Internet of Things (IIoT), manufacturing, and autonomous vehicles.

Here are six themes from this year’s AI World, as observed by Robotics Business Revieweditors attending the event:

1. Adding humanity to AI

During several general plenary keynotes, speakers noted that in order for AI to advance, more “human traits” needed to be added to the algorithms. Andrew Lo, a professor at the MIT Sloan School of Management, noted that a student of his referred to this as “artificial stupidity,” but then softened it by saying he prefers the term “artificial humanity.”

AI World conference Danny Lange Unity

Danny Lange from Unity discusses reinforcement learning models at the AI World conference in Boston. Source: AI World

In his AI World session covering algorithmic models of investor behavior, Lo noted that decision-making in humans relies a lot on emotions such as fear, greed, and anxiousness, and those traits would need to be factored into any AI algorithms.

In citing research about the psychophysiology of professional investors, he noted that the most successful trades would occur when skin conductivity was high, indicating tension on the part of the investors. Lo also noted that professional investors had the ability to “move on” from losses in comparison with amateur investors.

Danny Lange, vice president of AI and machine learning at Unity, talked about adding human traits like curiosity to reinforcement learning models to achieve more successful results.

When researchers programmed machine learning algorithms to achieve a specific goal — such as rewards when they found something in a maze of rooms — it wasn’t until the system was programmed to explore more that better results occurred.

However, Lange also noted that too much curiosity would be a problem, comparing it to someone watching Netflix on a TV and just continuing to watch show after show. He said that algorithms would need to add traits like impatience and boredom to offset an AI’s curiosity.

“There’s a lack of formal rigor in understanding deep neural networks,” observed Nicholas Roy, a professor at MIT’s Computer Science Artificial Intelligence Laboratory (CSAIL). MIT’s “Quest for Intelligence” combines the efforts of CSAIL students with the expertise of brain scientists, linguists, and social scientists to better understand intelligence itself, he said.

“It’s a core set of people looking at fundamental questions,” added Cynthia Breazeal, director of the personal robotics group at the MIT Media Lab and associate director of the Bridge for Strategic Initiatives in MIT’s Quest for Intelligence.

2. AI models will enhance software, back-office functions

At a session focusing on where investment funds are flowing, AI World speakers mentioned two specific areas of growth for the next few years. First, machine learning models will be used to enhance existing software services, making those more efficient and optimized. With lots of companies using cloud-based software services, efficiencies will improve as AI is added to the software.

AI World conference attendees expo hall

AI World attendees discussed AI and machine learning advances. Source: AI World

Second, many back-office functions are being automated through the use of AI and machine learning. Routine tasks such as bookkeeping, accounting, and expense management will become automated. One panelist noted that 80% of a bookkeeper’s job is routine tasks or functions.

Robotic process automation (RPA) provider and exhibitor UiPath cited a McKinsey study predicting that automation will add the equivalent of 2.5 billion full-time workers to the global workforce.

Like many in the robotics space, AI World presenters didn’t say whether the AI will replace humans in those jobs. Instead, they claimed that those workers would be freed up to handle more tasks that couldn’t be automated.

“Medicine is likely to see the biggest transformation in the near future,” said CSAIL’s Roy.

“If we don’t find ways to use data and analytics in healthcare, we’ll go broke,” asserted Dale Kutnick, senior vice president emeritus at Gartner Inc., referring to the increasing demand from aging baby boomers.

“I don’t believe that there will be no doctors in 30 years,” said John Mattison, chief medical information officer at Kaiser Permanente. “Even if 95% of today’s work may be automated, that will liberate humans for empathy … to do the things that got them into medicine in the first place.”

3. AI will need to rely on other AI

As machine learning models move closer to 100% confidence in their decision-making, more and more data is needed to feed those algorithms. Interestingly, one AI World speaker noted that when he asked his engineers about how much data would be needed to fix operational errors, they came back with the answer of, “We don’t know.”

Nathaniel Gates, CEO of Alegion, said that as the models get closer to 100% confidence, humans ill no longer be able to supervise the training of the models and that other AI models would be needed to assist the first AI model.

Without sounding the doom and gloom bell that you hear when people talk about “the singularity,” he said, machines talking with other machines will help those models get closer to the 100% confidence levels.

Gates also showed a chart that listed the confidence level needed to deploy specific AI models:

Model / application Confidence needed to deploy
Advertising sentiment 60%
Customer service chatbot 80%
Diagnostic medicine 90%
AI-augmented 911 95%
Autonomous vehicles 99%

 

“For good decisions, you want to avoid expertise bias and not need billions of images,” said Heather Ames Versace, chief operating officer of Neurala, whose Brain Builder product is designed to accelerate AI development by tagging and annotating simultaneously. “You need the right data in the right way.”

“Humans are still involved more in robotics development than in AI,” said Phil Duffy, vice president of innovation at Brain Corp. “And in usage, Brain designed its robots used by Walmart to include janitors in the operational loop. Keeping humans in the loop helps with adoption.”

4. In the world of IoT, AI means optimization

In a discussion about using deep learning in industrial applications, AI World panelists described using neural networks to help optimize energy usage within “smart facilities.” They also mentioned adding sensors and retrofitting older buildings to take advantage of the latest technologies.

While optimization on a heating or cooling system could mean just shutting it off during certain times of the day, presenters also mentioned the need for “comfort.” This led to discussion of occupancy levels and where people were located in a building to make sure they weren’t complaining that an office is too hot or too cold.

One AI World speaker said deep learning is becoming part of what he called “IoP” – the Internet of People. By giving employees a wearable tracker, employers could track where workers moved during the day and what types of actions they were doing.

Through this analysis, retailers and warehouse companies could determine if shorter employees were trying to reach products located on higher shelves, indicating a need to rearrange operations for better efficiency.

Session participants also mentioned that “digital twin” technology wasn’t just for manufacturing. Simulation software can be used to make a digital twin of an entire building or even a process.

One speaker mentioned that a logistics company was using a digital twin at its innovation center to test the designs of a new distribution center, adding simulations such as what would happen to its processes if products arrived late.

5. AI at the edge

Computing at the edge of networks will continue to become more important, especially for devices and machines that have difficult connectivity options for cloud-based AI processing. Several companies, including Germany’s Bragi, displayed edge AI products and services at the show.

However, some AI World attendees noted that processing at the edge and IIoT still have limitations, even with approaching 5G connectivity.

“Businesses assume that big data is all together and ready for analysis, but it’s not static; it’s a living, breathing thing,” said Raj Minhas, vice president and director of the Interactions and Analytics Lab at PARC.

Autonomous mobile robots indoors cannot use GPS for localization and positioning like self-driving cars, Duffy told Robotics Business Review. As a result, they need to map differently, stop instantly, and “can’t always solve edge cases from remote observation,” he said. “Indoor navigation is still a complex problem.”

6. Ethics a consideration at AI World, but standards also important

Several AI World speakers said the “explainability of AI” would be big in the next few years – not just for legal teams, but to make sure that humans understood why certain decisions were being made. In the healthcare space, a few panelists mentioned that the “why” of a decision would be more important for doctors than the “what” decision or treatment was made.

MIT’s Lo mentioned that humans often make decisions based on demographic data points, but often the decisions have innate biases and very sparse data. “It’s human nature that we are able to make split-second decisions based on so little data,” he said.

The “Morning Coffee” panel on “The Future of AI: Views From the Frontier” also discussed the goal of “democratizing” AI to non-Ph.D.s, as well as concerns about how to build systems that respect privacy, particularly of children, as systems such as Amazon Alexa and Google Home constantly gather user data.

“Informed consent and transparency are at the core of ethical AI,” said MIT CSAIL’s Roy.

AI World panel on bias and AI

AI World’s Jeff Orr moderates a panel on “Removing Bias and Explainable AI.” (Click hereto enlarge for names, titles.)

“We need to involve all groups — data science and ethics — in interdisciplinary efforts,” said Arif Virani, chief operating officer at DarwinAI, in another panel.

“We need best practices for exposing and sharing flaws,” said Matthew Carroll, CEO at Immuta. “It’s not about government regulations but how to build standards.”

“Regulations should be at the level of the outcome,” said PARC’s Minhas, in reference to autonomous vehicles and AI for state and federal rules, which lag behind technology innovations. As an example of learning about AI behavior, he described self-driving cars turning left more often during purple skies, ultimately because they were turning into home driveways at sunset.

“We need to move data science from skunk works to an engineering discipline, with guidelines and best practices,” Minhas said.

Avoiding negative bias is also important as AI is increasingly used in healthcare, insurance, lending, and criminal justice, noted Abby Everett Jaques, a postdoctoral associate in the MIT Department of Linguistics & Philosophy.

“Ethics should not be an add-on at the end; it should be part of the collaborative development process,” she said. “Little projects seem benign, but we should be aware of how they will connect with the larger ecosystem.”

“Instead of trying to understand a deep neural network from Layer 132, we should test AI like human on a job interview,” said Neurala’s Versace. “You wouldn’t give a job candidate an MRI. Based on the data inputs, what outcomes can it produce?”

“Government has a lot of learning to do, and some vendors have to stop overselling AI,” said Versace. “We’re still an early-stage industry, and we need to work together.”

Editor’s Note: Senior Editor Eugene Demaitre contributed to this article

Read  Source Article Robotics Business Review

#AI #EventCoverage #Health&Medical #ArtificialIntelligence #DeepLearning #MachineLearning #Events #IoT #Software #News

 

In recognition of the increasing importance of artificial intelligence (AI) on future innovations, Samsung Electronics has been investing in and expanding its AI capabilities by establishing seven Global AI Centers in 2018. Founded in May, Samsung AI Center-Moscow (SAIC-Moscow) has already made marks winning a series of highly prestigious Global AI competitions.

 Pavel Ostyakov, one of the researchers from SAIC-Moscow, won first place among 110 teams at the “Inclusive Images Challenge,” a Kaggle1 competition hosted by Neural Information Processing Systems (NeurIPS) 2018, which took place in Montreal, Canada from December 3rd to the 8th. NeurIPS, formerly known as NIPS, is the world’s largest conference in the field of AI. As of 2017, a total of 8,000 people participated at the event. Apart from machine learning and neuroscience, experts from many related research fields such as cognitive science, computer vision, statistical linguistics, and information theory actively participate in the conference. Winning this challenge was a significant achievement for both Samsung and Pavel as he has been named the Competitions Grandmaster in the Kaggle category of data science expertise, which is the highest tier possible. Pavel also has the honor of being ranked one of the world’s top five scientists on Kaggle.

 In the “Inclusive Images Challenge”, participants developed image recognition and classifier models that can successfully perform image classification tasks even when the test images are geographically and culturally different from the previously shown images in which the recognition models were trained for.

 

(Left image, from left) Pavel Ostyakov, a Researcher at Samsung AI Center-Moscow (SAIC-Moscow), and Jin Wook Lee, the Head of Samsung R&D Institute Russia. (Right image) A snapshot of SAIC-Moscow’s opening ceremony, which took place in May of this year

For example, an image classifier may fail to properly apply “wedding” related labels, such as “bride,” “groom,” and “celebration,” to an image, if a couple is not wearing traditional western European wedding attires or colors. This challenge attempts to address the biases that exist in many of the most popular training datasets. Through this challenge, researchers can identify ways to teach image classifiers by generalizing the accumulated data and apply them in new geographic and cultural contexts. The expectation is that the scientific community will make even more progress in inclusive machine learning that benefits everyone, everywhere.

Samsung has been a strong proponent for inclusive and fair AI in which the two principles are deeply incorporated into its AI developments, emphasizing the need for a truly global and bias-free artificial intelligence as it plays a bigger role in society. Samsung’s joining of the Partnership on AI (PAI) in November is also a part of this effort.

Last September, the SAIC-Moscow team also participated in “The 2nd YouTube-8M Video Understanding Challenge,” hosted by the European Conference on Computer Vision (ECCV) 2018. ECCV is one of the world’s top research conferences in the computer vision area and is held biennially. In this Kaggle competition, researchers were provided with public YouTube-8M training and validation datasets that consists of millions of videos with labels, and then asked to develop classification algorithms, which accurately predicts the labels of 700,000 previously unseen YouTube videos. During the competition, the SAIC-Moscow team utilized a unique approach in its complex model and data analysis, placing second by a very narrow margin.

An exterior view of the White Square Business Center, where SAIC-Moscow is located

 “We love these competitions because they provide us with opportunities to measure ourselves to participate with the best in the AI industry,” said Pavel Ostyakov at SAIC-Moscow. “Researchers at Samsung are obsessed with making AI a part of everyday lives. So, it is exciting to take part in the challenges where we can contribute our skills to develop the technology.”

The global AI centers reflect Samsung’s commitment to next generation AI development. Besides Russia, there are six others located across the globe – Korea, Silicon Valley and New York in the U.S., Toronto and Montreal in Canada, and Cambridge, the U.K. Each location is focused on a different area of strengths and leverages its unique characteristics. These AI centers are playing a pivotal role in implementing Samsung’s vision for human-centric AI technologies and products.

1An online community allowing users to find and publish datasets, explore and build models in a web-based data-science environment, work with others, and enter competitions to solve data science challenges.

Read Source Article Samsung

#AI #Samsung #datasets #FutureInnovations #GlobalAI

These days, staying up to date on cutting-edge technologies is critical to company relevancy. For example, recent advances in artificial intelligence and virtual reality have made major waves in the way some businesses operate. The company that knows about new tech earlier has a better chance of staying ahead of the curve and its competitors.

As groundbreaking advances are made in the realms of AI and VR, many are speculating on how these technologies will reshape both everyday living and the way businesses operate. We asked 14 members of Forbes Technology Council to highlight the ways they foresee AI and VR technologies changing the world.

1. Enhanced Health Care

Artificial intelligence will change how medicine is developed, how diseases are diagnosed, and how medicine is prescribed and applied. To continue to speed this transformation we need greater availability of data, balanced regulation and public education, at a minimum. - Mohamad ZahreddineTrialAssure

2. Custom Home Builds

I think these technologies can help us move past tract homes and find ways to build homes in a more customized, affordable way based on personal budgets and situations — that also may help us solve the housing crisis. - Jon BradshawCalendar

3. Better Training And Therapy

Virtual reality apps are already making great strides in the areas of training through more realistic simulations and therapy applications. If the promising results are any indicator, this will be an area to watch and a great example of tech directly improving our non-digital lives. - Matthew WallaceFaction, Inc.

Artificial intelligence and virtual reality combined will revolutionize education through immersive personalized learning. We’re already seeing this for specialized training, and over the next few decades, it will broaden to cover core K-18 and master’s level education. - Bret PiattJungle Disk

5. More Accurate Predictive Modeling

Understanding why something has happened or will happen has been important in many disciplines. In the past, predictive modeling suffered from a lack of data and understanding of the dependencies of the data. With more information than ever being collected by companies, we can now apply AI techniques, specifically machine learning, and use a more holistic process to understand and predict events. - Chris Kirby, Retired

6. Improved Focus On High-Value Activities

I’m really interested in seeing how artificial intelligence technologies help improve human focus on high-value activities. In recruiting, for example, recruiters are already able to use AI to quickly find, evaluate and engage top prospects for open jobs — letting them spend their time convincing people to join their company instead of spending substantial time searching for them. - Xinwen ZhangHiretual

7. New Breakthroughs From Better Data Correlation

We are generating vast amounts of data on a daily basis. Yet most companies still spend considerable time hypothesizing what the data will tell them and then attempting to prove themselves right. AI finds correlations with data that we can never anticipate. These unexpected discoveries are already leading to significant breakthroughs in medicine, law, finance and security. - Kathy KeatingApostrophe, Inc.

8. Customer Centricity

These technologies will help companies to understand their customers better and provide personalized products and services, as well as allow them to engage with the customers in their environment. - Thiru SivasubramanianSE2, LLC

9. Small-Scale Life Improvements

Artificial intelligence already drives self-driving cars, unlocks your phone and types for you. In the coming years, it will change the world in less visible ways by making sure rental cars are available even when there’s an eclipse, ordering inventory in advance or adjusting staffing automatically. This will reduce costs for companies — and thus for consumers — while improving lives in small ways. - Alexander ShartsisPerfect Price

10. Data Analytics In Near-Real Time

Key AI techniques fall into the following areas: machine learning, computer vision, NLP robotics, deep learning and cognitive computing. The biggest impact is going to be the integration and convergence of AI, internet of things and distributed ledgers, where the output of intelligence gleaned from Big Data is delivered to the members of the distributed network in near-real time. - Rahul SharmaHSBlox

11. Augmented Intelligence

Artificial intelligence and virtual reality will combine to provide augmented intelligence, helping humans think better. There are two problems being solved here. First, offloading more and more cognitive tasks to machines frees humans to focus on higher and higher value thinking. Second, improving the interface between humans and machines allows for better and faster communication. - Chris GrundemannMyriad Supply

12. Creation Of The Chief AI Officer Role

Companies did not have a CTO or CIO before information technology became what it has become today. AI is going to do the same thing. In the future, every company will have a Chief AI Officer, and around half a workforce of a typical company will be algorithms. I call algorithms a workforce and not assets because like employees they will need to be trained, improved and made efficient over time. - Amit JnagalInfrrd

13. Better Data-Driven Decisions Via Natural Language Generation

Organizations collect massive amounts of data but often fail to gain clear insight. Natural language generation, a subset of AI, is the solution to definitively communicate the current problem using data so companies can make data-centric, strategic next steps. When a company has full understanding, it gains the ability to make better data-driven decisions. It gains the power to change the world. - Marc ZiontsAutomated Insights

14. Focused ‘Smart’ Applications

Don’t expect that AI will suddenly and fundamentally change your world. Instead, smaller, highly focused “applications” will emerge incrementally. Your house will get smarter about when and how it consumes electricity. You’ll get alerted that grandma’s daily behavior has changed in a way that may merit a doctor’s visit. More spam emails and phone calls will get blocked before they reach you. - Kent DicksonYonomi

Forbes Technology Council is an invitation-only, fee-based organization comprised of elite CIOs, CTOs and technology executives. 

Read Source Article Forbes

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#AI #VR #CIOs #CTOs #Technology #SmartApplications #Forbes #DataAnalytics #NaturalLanguageGeneration

 

Learn the basics and keep up with the latest news in data science, machine learning and artificial intelligence by listening to these great podcasts

1. PlayerFM

Artificial intelligence is more interesting when it comes from the source. Each week, Dan Faggella interviews top AI and machine learning executives, investors and researchers from companies like Facebook, eBay, Google DeepMind and more - with one single focus: Gaining insight on the applications and implications of AI in industry. Follow our Silicon Valley adventures and hear straight from AI's best and brightest.

Area(s) of focus: #AI #MachineLearning #Deeplearning

Link: https://player.fm/series/artificial-intelligence-in-industry-with-dan-faggella

2.Data Skeptic

A long-time favorite of mine and a great starting point on some of the basics of data science and machine learning. They alternate between great interviews with academics & practitioners and short 10–15 minute episodes where the hosts give a short primer on topics like calculating feature importance, k-means clustering, natural language processing and decision trees, often using analogies related to their pet parrot, Yoshi. This is the only place where you’ll learn about k-means clustering via placement of parrot droppings.

https://dataskeptic.com/podcast

3. The O’Reilly Data Show

This podcast features Ben Lorica, O’Reilly Media’s Chief Data Scientist speaking with other experts about timely big data and data science topics. It can often get quite technical, but the topics of discussion are always really interesting.
O’Reilly entry on this list is one of the newer podcasts on the block. Hosted by Jon Bruner (and sometimes Pete Skomoroch), it focuses specifically on bots and messaging. This is one of the newer and hotter areas in the space, so it’s definitely worth a listen!

https://www.oreilly.com/topics/oreilly-data-show-podcast

4. Concerning AI

Concerning AI offers a different take on artificial intelligence than the other podcasts on this list. Brandon Sanders & Ted Sarvata take a more philosophical look at what AI means for society today and in the future. Exploring the possibilities of artificial super-intelligence can get a little scary at times, but it’s always thought-provoking.

https://concerning.ai/

5. Data Stories

Data Stories is a little more focused on data visualization than data science, but there is often some interesting overlap between the topics. Every other week, Enrico Bertini and Moritz Stefaner cover diverse topics in data with their guests. Recent episodes about data ethics and looking at data from space are particularly interesting.

http://datastori.es/

6. Linear Digressions

Hosted by Katie Malone and Ben Jaffe, this weekly podcast covers diverse topics in data science and machine learning: talking about specific concepts like model theft and the cold start problem and how they apply to real-world problems and datasets. They make complex topics accessible.

http://lineardigressions.com/

7. Learning Machines 101

Billing itself as “A Gentle Introduction to Artificial Intelligence and Machine Learning”, this podcast can still get quite technical and complex, covering topics like: “How to Catch Spammers using Spectral Clustering” and “How to Enhance Learning Machines with Swarm Intelligence”.

http://www.learningmachines101.com/

8. Talking Machines

In this podcast, hosts Katherine Gorman and Ryan Adams speak with a guest about their work, and news stories related to machine learning. A great listen.

http://www.thetalkingmachines.com/

9. This Week in Machine Learning & AI

This Week in Machine Learning & AI releases a new episode every other week. Each episode features an interview with a ML/AI expert on a variety of topics. Recent episodes include discussing teaching machines empathy, generating training data, and productizing AI.

https://twimlai.com/

10. Partially Derivative

Hosts Chris Albon, Jonathon Morgan and Vidya Spandana all experienced technologists and data scientists, talk about the latest news in data science over drinks. Listening to Partially Derivative is a great way to keep up on the latest data news.

http://www.partiallyderivative.com/

 

11. NVIDIA

Artificial intelligence has been described as “Thor’s Hammer“ and “the new electricity.” But it’s also a bit of a mystery – even to those who know it best. We’ll connect with some of the world’s leading experts in AI, deep learning and machine learning to explain how it works, how it’s evolving and how it intersects with every facet of human endeavor, from art to science. We release new episodes about every other week

https://blogs.nvidia.com/ai-podcast/

12. Machine Learning Guide

Machine Learning Guide Teaches the high level fundamentals of machine learning and artificial intelligence. I teach basic intuition, algorithms, and math. I discuss languages and frameworks, deep learning, and more. Audio may seem inferior, but it's a great supplement during exercise/commute/chores. Where your other resources provide the machine learning trees, I provide the forest. Consider me your syllabus. At the end of every episode I provide high-quality curated resources for learning each episode’s details.

http://ocdevel.com/mlg

13. Gigaom

Published and sponsored by Gigaom, Voices in AI is a new podcast that features in-depth interviews with the leading minds in artificial intelligence. It covers the gambit of viewpoints regarding this transformative technology, from beaming techno-optimism to dark dystopian despair.

The format features a single guest in an hour-long one-on-one interview with host Byron Reese. Featuring today’s most prominent authors, researchers, engineers, scientists and philosophers, the podcast explores the economic, social, ethical and philosophical implications of artificial intelligence. Conversation centers on familiar terrain relating to jobs, robots, and income inequality, yet also reaches more far-flung topics such as the possibility of conscious machines, robot rights, weaponized AI, and the possible re-definition of humanity and life itself. With a topic as rich as AI, there is seldom a slow moment.

The goal of the show is to capture this unique moment in time, where everything seems like it might be possible, both the good and the bad. Artificial intelligence isn’t overhyped. The optimists and pessimists believe one thing in common: That AI will be transformative. Voices in AI strives to document that transformation.

https://twitter.com/voicesinai?lang=en

https://voicesinai.com/


14. MIT Artificial Intelligence

Eric Schmidt was the CEO of Google from 2001 to 2011, and its executive chairman from 2011 to 2017, guiding the company through a period of incredible growth and a series of world-changing innovations. Video version is available on YouTube. If you would like to get more information about this podcast go to lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, or YouTube where you can watch the video versions of these conversations. more


https://lexfridman.com/ai/

15. Brain Inspired

Learn how AI techniques, like machine learning, deep learning, and neural networks can help you explore your data, generate hypotheses, and publish in top tier journals

https://braininspired.co/podcast/

16. MIND & MACHINE

MIND & MACHINE is a weekly interview show with people at the forefront of transformational technologies, futurist ideas and the sociological impact of these exponential changes.

We focus on cultural forces and technologies that will transform our world: Artificial Intelligence, Robotics, IoT, Space Exploration, Virtual & Augmented Reality, Life Extension, Blockchain, Cryptocurrencies, BioTech, Transhumanism and more.

https://www.mindandmachine.io/

17. AI Today by Cognilytica

Cognitive technologies are advancing at a rapid pace and it’s hard to always keep up to date on everything. That’s why Cognilytica has compiled a list of 20 AI-focused podcasts to help keep you up to date on everything going on related to AI, ML, and cognitive technologies

https://www.cognilytica.com/category/podcasts/

 

18. NLP Highlights by the Allen Institute for Artificial Intelligence:

Adversarial Learning
Adversarial Learning is a podcast from AI2 team member Joel Grus about data, data science, and science.

You can listen to Adversarial Learning on the podcast's website or iTunes.

NLP Highlights
NLP Highlights is AI2's podcast for discussing recent and interesting work related to natural language processing. Matt Gardner and Waleed Ammar, research scientists at AI2, give short discussions of papers, and occasionally interview authors about their work.

You can listen to NLP Highlights on SoundCloud or iTunes.

https://allenai.org/podcasts/podcasts-all.html

19. AI at Work By Talla

AI at Work takes a look into AI trends and the future of AI in the enterprise, hosted by Talla's CEO Rob May. There are a lot of misconceptions in this space, even around the basics of what AI is and what you can do with it. AI at Work is educational for business leaders, providing insight on how to think about and effectively deploy AI in your organization.

https://itunes.apple.com/us/podcast/ai-at-work/id1399642701?mt=2

20.The Architecht Show by Architecht

A weekly podcast about the business of cloud computing, artificial intelligence and data science. Hosted by Derrick Harris.

http://architechtshow.com/

21. AI with AI by CNA

AI with AI explores the latest breakthroughs in artificial intelligence and autonomy, as well as their military implications. Join experts Andy Ilachinski and David Broyles as they explain the latest developments in this rapidly evolving field.

The views expressed here are those of the commentators and do not necessarily reflect the views of CNA or any of its sponsors. Episodes are recorded a week prior to release, and new episodes are released every Friday. Recording and engineering provided by Jonathan Harris.

https://www.cna.org/news/AI-Podcast

22. Practical AI

Making artificial intelligence practical, productive, and accessible to everyone. ... and Data Science and what they hope to accomplish as hosts of this podcast.

https://changelog.com/practicalai

23. iProspect

A.I. and Machine Learning podcast features in-depth analysis and insight from iProspect's data experts on the impact that these technologies will have on performance media, as well as an interactive Machine Learning game. Listen to Managing Director Jack Swayne, Data Scientist Josh Carty and Director of Data & Technology Products Sophie Wooller demonstrate the capability and possibilities that A.I and Machine Learning can have for businesses.

https://www.iprospect.com/en/gb/insights/podcasts/ai-and-machine-learning-episode-one/

https://www.linkedin.com/company/iprospect

https://twitter.com/iprospectuk

24. Sentient

 

Sentient is proud to present its new podcast series: “The Optimization Podcast: Experts in CRO and Website Testing.” In this series, we interview veteran website testers and CRO experts to demystify the art of website optimization. Our experts come from a variety of backgrounds, from digital marketing teams of midsize and enterprise companies, to digital and creative agencies that serve a number of different clients, to specified conversion consultancies that have spent years optimizing website conversions.

https://www.sentient.ai/podcasts

25. AI Supremacy

 

Minh Le, CEO at CityLink.AI, discusses integrating connected technology into communities. Daniel Wagner, CEO at Country Risk Solutions, talks about whether the benefits of artificial intelligence outweigh anxieties. Diana Cooper, Senior VP of Policy and Strategy at PrecisionHawk, explains how industries are being revolutionized by drones. Dr. Aleksandra Mojsilovic, Head of AI Foundations at IBM, discusses applying AI technology with human intelligence. And we Drive to the Close of the markets with Andrew Slimmon, Senior Portfolio Manager at Morgan Stanley Investment Management. Hosts: Carol Massar and Jason Kelly. Producer: Paul Brennan

https://ai-supremacy.com/news/?category=podcast#

 #AI #MachineLearning #BigData #Technology #Podcasts #Artificialintelligence #DataScience

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