I recently started an AI-focused educational newsletter, that already has over 100,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Quantifying trust and fairness is one of the most important challenges to ensure the mainstream adoption of deep learning systems. But what does trust truly means in the context of deep learning systems?

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According to Gartner, artificial intelligence is still in the early phases of the hype cycle. Among the 37 types of AI on its chart, only speech recognition and GPU accelerators have reached the plateau of productivity. Despite the fact that many AI technologies are too new for mainstream adoption, manufacturing leaders are already stuck in a rut with these projects, according to a new survey from Plutoshift. Plutoshift found that manufacturing professionals are having trouble with almost every aspect of AI projects from defining realistic outcomes and collecting data to getting internal buy-in. The Gartner Hype Cycle for Artificial Intelligence 2019 examines the stream of innovations and trends in the AI sector and scopes AI plans.

Artificial intelligence (AI) looks to be the most disruptive class of technologies in driving digital business forward during the next decade. Yet, even amongst the most tech-savvy professionals, there is divided-opinion over what AI can or cannot be defined as, and what it can and can't achieve. The most exciting aspect of applying AI in the legal profession lies in the automation of repetitive and straightforward tasks, like eDiscovery or judicial bill review, while enabling human experts to improve results beyond what machines or people could do alone. This combination allows for enhanced productivity while driving significant time and resource savings. This piece will clarify what AI is, how it's being used today, and how it can improve legal operations -- now and in the future.

Artificial intelligence (AI) is proving to be the most significant technological advancement across all industries in recent decades. While we're still years away from the robotics side of AI, the machine learning (ML) sector has exploded by helping companies with everything from improving customer retention rates to driving enhanced insights from big data and even mitigating supply chain risks. With the global machine learning market anticipated to grow from $1.4B in 2017 to $8.8B by 2022 according to a recent report by Research and Markets, here's a look at where those investments are headed and what it means for increasingly in-demand machine learning talent. Last year, Amazon introduced us all to Alexa in the workplace, but this voice-activated, AI-powered device is only the beginning. Natural language processing (NLP), made possible through machine learning, helps computers, systems, and solutions better understand the context and meaning of sentences.

Blockchain, hyped as the technology is at the moment, has not only quite some potential but has matured considerably over the past months. Still, after having critically looked at some promising use cases of blockchain throughout the last articles of this column, it is time move back to its original theme for a change. After all, the main theme of this column is Artificial Intelligence Trends and Tools. And bots, AI, and machine learning are exciting topics in their own right, let apart in their combination. Let's have a look at some chatbot and digital assistant developments … Since these are other technology areas that are also maturing fast.

The high-tech industry has been a leading adopter of artificial intelligence (AI), so much so that it influences the digital transformation trends in most other industries. Technology giants are not just building AI solutions, they are acquiring smaller AI companies to build more capabilities and finding new use cases outside the IT department, in our offices, hospitals, and homes. Yet, as per leading research reports these firms are still lagging in the overall AI adoption when compared with other digitization efforts. This paper attempts to find the factors behind the high-tech industry's failure to scale and the best practices the industry can adopt to encourage mainstream adoption. It has been almost seven decades since Alan Turing first envisioned a'thinking' machine that could potentially carry on a conversation with a human that was indistinguishable from a human-to-human interaction. Since then, AI has seen several periods of crests and troughs.

IT executives often find themselves in a balancing act to achieve two things for business leaders -- quick IT project success, perhaps a proof-of-concept project, and then also preparing the larger organization for the long-term potential and value of an emerging technology. Artificial intelligence and all the technologies that go with it fall into this category. Your IT organization may be at the start of the journey with some initial projects. Or you may be working to integrate the value of AI more deeply into your organization's technology and process infrastructure. Yet you also know that the biggest value will come from long-term investments in this emerging field.

The Internet of Things is slowly permeating every aspect of our lives. If you don't already own an IoT device, you've surely heard of them. From smart thermostats to smart coffee makers, IoT devices are slowly but surely garnering mainstream adoption. In addition, virtual assistants (such as Siri, Cortana, and Alexa) are only making this technology easier to adopt. But, these devices are far from perfect.

It's the beginning of September (and no, I don't know how that happened either), and as the summer lull winds to a close and we prepare for a renewed frenzy of activity, it's a good moment to take stock of the year so far. Last month, two different articles were published looking back over some of the key trends we've seen in 2018 with regard to emerging technology and digital transformation, and comparing them to what was predicted. One was a piece by Forbes contributor Daniel Newman of CMO Network, who revisited his predictions for digital transformation in 2018 in light of the past eight months, to see where we are with some of 2018's most potentially transformative technologies. It takes a broadly optimistic view of the technologies that are meant to be shaking up the digital world in 2018, with a few caveats. The other was by The Register's Andrew Orlowski, written in response to the publication of Gartner's annual'Emerging Technologies Hype Cycle', which revealed that a number of the most "hyped" technologies from last year have vanished from this year's chart.