Greetings Artificial Intelligence and IoT Weekly News Readers!

Dropping another round of interesting AI & IoT articles from the Internet into your INBOX. For reference, you can always visit our website and browse the archives and other useful content. On to the latest news!

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I'm super excited to be speaking this week at the University of Saint Thomas Selim Center for Lifelong Learning! The topic of my talk is What is the Artificial Intelligence of Things (AIoT) and How It is Changing Our Lives. I do speaking engagements often, so if you are interested in booking me to speak at your event or in your business on Artificial Intelligence, Machine Learning and/or Internet of Things, please reach out.

I just released a new podcast episode on bringing Deep Learning to microcontrollers with TinyML. I was super excited at the opportunity to meet Pete Warden and interview him as he's the author of an awesome book called TinyML, published by O'Reilly.

Finally, we had our Applied AI Meetup last week. Amazing presentation on The Feminist Dataset by Caroline Sinders. Visit our YouTube page and subscribe to watch it!

I hope you enjoy this issue, and please reach out to me anytime to discuss if you would like to present on a topic related to IoT, ML, and AI at a future meetup, conference or podcast.

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The AIoT Revolution: How AI and IoT Are Transforming Our World

The AIoT has the potential to transform industries and society, and it is already starting to have an impact. This article will explore the principles of AIoT, its benefits, and its current use.

spe.org

AIoT and Edge Analytics: A Powerful Combination

In many cases of AI integration, activities need to occur locally to act fast. For example, if the AI system receives an alert about a machine fault, the AI system may make the decision to stop the machine to avoid product damage. By integrating the AI system at the edge instead of in the cloud, latency issues can be avoided, meaning the machine is switched off more quickly and fewer products are damaged.

enterpriseai.news

AI Models Can Now Continually Learn From New Data on Intelligent Edge Devices Like Smartphones and Sensors

Microcontrollers, miniature computers that can run simple commands, are the basis for billions of connected devices, from internet-of-things (IoT) devices to sensors in automobiles. But cheap, low-power microcontrollers have extremely limited memory and no operating system, making it challenging to train artificial intelligence models on "edge devices" that work independently from central computing resources.

sciencedaily.com

The White House Just Moved to Hold AI More Accountable

The white house has unveiled a new AI bill of rights. It outlines five protections Americans should have in the age of AI accountability. Critics say the plan lacks teeth and the US needs tougher regulation. The OSTP’s AI bill of rights is “impressive,” says Marc Rotenberg. It is a very good starting point to move the US to a place where it can carry forward on that commitment. A new bill will allow consumers to sue companies for damages.

technologyreview.com

Google’s New AI Can Hear a Snippet of Song - and Then Keep on Playing

AudioLM generates natural-sounding sounds without the need for human annotation. The technique shows promise for speeding up the process of training AI to generate audio. It could eventually be used to auto-generate music to accompany videos. AudioLM is trained to learn what types of sound snippets occur frequently together. It also has the advantage of being able to learn the pauses and exclamations that are inherent in spoken languages but not easily translated into text. Audio-generated music could be used to provide more natural-sounding background soundtracks for videos and slideshows.

google.com

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appliedaiconf.com

New Hires Won’t Fix the AI Skills Gap

Hiring talent is a terrible place to start en route to fulfilling AI aspirations. A hiring-first approach to talent sourcing can introduce a difficult work dynamic. The investments come at a time when the data center industry booms and the office market struggles to welcome back employees.

Simply hiring talent is a terrible place to start en route to fulfilling AI aspirations, according to Sreekar Krishna, head of AI for KPMG US. “An institution cannot become an AI company just by going and hiring data scientists,” Krishna said. “Worst strategy ever.”

ciodive.com

Regie Secures $10M to Generate Marketing Copy Using AI

Rege.AI, a startup using openai’s gpt-3 text-generating system, raises $10 million. The new investment comes as VCs see a growing opportunity in copy-generating adtech companies. Regie is a more vertical platform that caters to go-to-market teams in the enterprise while combining text, images, and workflows into a single glass pane. The company currently has more than 70 software-as-a-service customers.

techcrunch.com

Can Artificial Intelligence Help Identify Best Treatments for Cancers? LSU Researchers Say Yes

Canceromicsnet is a new drug discovery engine run by Artificial Intelligence. It can predict how specific cancer responds to a particular drug. A team of researchers from the school of veterinary medicine and the Center for Computation & Technology created the engine. Canceromicsnet can help scientists match cancer cell lines with the drugs likely to be the most toxic to their growth and genetic profile. The ultimate goal, he said, is to expand their research to potentially apply it in clinical settings.

theadvocate.com

Why Is It So Hard To Deploy AI?

The definition of AI hints at one of the reasons: it is “goal-directed adaptive behavior”. The third category of AI deployment is humanization, which means using technologies like deepfakes and natural language models to mimic the behavior of humans. The sad fact is that implementing AI is not easy. Most projects fail because of under-investment or because of misunderstanding about what it is actually capable of.

forbes.com