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Managing AI Risks. Multiple Stakeholders Need Access to the Right Data and Insights

In this contributed article, Dr Stuart Battersby, Chief Technology Officer of Chatterbox Labs, offers two important points while explaining how it is critical that issues of AI risk are addressed with clear, transparent, and up to date data into the operation of the AI model.

Addressing AI Trust, Systemic Bias & Transparency as Business Priorities

Our friend Dr Stuart Battersby, CTO of Chatterbox Labs (an Enterprise Al Company), reached out to us to share how his company built a patented AI Model Insights Platform (AIMI) to address the lack of explainability & trust, systemic bias and vulnerabilities within any AI model or system.

How Artificial Intelligence Will Help Decide This Season’s NFL Pizza Delivery War

In this contributed article, Campbell Brown, CEO & Co-Founder of PredictHQ, analyzes how companies that double down on smarter ways to accurately forecast demand during NFL season can be successful – which in today’s world, represents an opportunity for competitive advantage that businesses won’t want to pass up.

AI Under the Hood: FunCorp.

In this new edition of our popular “AI Under the Hood” column, FunCorp Co-founder and CIO Denis Litvinov does a deep dive into his company’s use of AI on social media networks. AI is a key component of the popular social networks you use every single day. FunCorp develops and operates UGC services for different geo and audience niches.

As AI Evolves, No Need For Overhaul of U.S. Patent System … Yet

In this contributed article, Daniel J. Schwartz, partner in Nixon Peabody’s Intellectual Property group, and Paulina M. Starostka, an associate in Nixon Peabody’s Intellectual Property group, discuss the explosion of recent AI-related patent grants. As this rise has occurred, so have interesting questions concerning patentability, inventorship, ownership, and disclosure issues. To address these (and other) concerns, the USPTO launched its Artificial Intelligence Initiative in 2019, engaging the innovation community and experts to determine whether AI required any changes to the U.S. Patent system.

Infographic: AI Innovators – The Countries & Companies Leading in AI Patents

With Artificial Intelligence (AI) continually progressing and developing, it’s unsurprising that many companies are aiming to lead in the AI technology market – especially given that the global AI market is on track to be worth over $118 billion by 2022. But which companies are currently leading the way for innovation when it comes to AI? To find out, our friends over at RS Components analyzed AI patent applications in order to find out which brands are at the forefront of AI innovation.

Deepgram Pioneers Novel Training Approach Setting New Standard for AI Companies

Artificial intelligence has made astonishing technological advances in recent years and more companies are turning to AI to improve internal functions and unlock the potential of enterprise datasets. IDC has characterized AI as “inescapable” and estimates that by 2025, at least 90% of new enterprise apps will embed AI. But getting to the right models to effectively power AI is hard – and especially hard for speech. Today in order to address these needs, Deepgram has announced Deepgram AutoML, a new training capability that streamlines AI model development, reducing manual cycles for data scientists while giving them the best accuracy humanly possible.

Will Robot Lawyers Lie Like Humans?

In this contributed article, Pawel Stopczynski, Researcher and R&D Director at VAIOT, believes that armed with technology, lawyers are already more efficient than before. The trend toward digitizing law is poised to continue as robot lawyers become smarter and more capable. The question is, will robot lawyers lie like humans?

Book Review: Deep Reinforcement Learning Hands-On

RL is a hugely popular area of deep learning, and many data scientists are exploring this AI technology to broaden their skillet to include a number of important problem domains like chatbots, robotics, discrete optimization, web automation and much more. As a result of this wide-spread interest in RL, there are many available educational resources specifically tailored to this class of deep learning – boot camps, training certificates, educational specializations, etc. But if you’re a data scientist who has been programming in Python for a while, and has some experience with other forms of deep learning using a framework like TensorFlow, then maybe this new book, “Deep Reinforcement Learning Hands-On,” by Maxim Lapan, might be a great way to kick-start yourself into becoming productive with RL.

Little Known Artificial Intelligence Secrets: What Unsupervised Learning Really Means

In this contributed article, editorial consultant Jelani Harper takes a look at the chief value proposition of unsupervised learning—for machine learning models to explore and categorize data based on their findings as opposed to stratifications imposed by humans—and how it is immensely useful for everything from general data discovery to targeted use cases like precision medicine.