Whether it's auto-extracting information from a scanned receipt for an expense report or translating a foreign language using your phone's camera, optical character recognition (OCR) technology can seem mesmerizing. And while it seems miraculous that we have computers that can digitize analog text with a degree of accuracy, the reality is that the accuracy we have come to expect falls short of what's possible. And that's because, despite the perception of OCR as an extraordinary leap forward, it's actually pretty old-fashioned and limited, largely because it's run by an oligopoly that's holding back further innovation. OCR's precursor was invented over 100 years ago in Birmingham, England by the scientist Edmund Edward Fournier d'Albe. Wanting to help blind people "read" text, d'Albe built a device, the Optophone, that used photo sensors to detect black print and convert it into sounds.

At Deep Instinct, we pride ourselves on preventing what other cybersecurity solutions can't even detect. To make a long story short, and to paraphrase my last blog post on Machine Learning vs Deep Learning [READ], we achieve this through a combination of AI, machine learning and deep learning. Deep Learning is not a technology that is widely used by companies in production yet, with only a select few managing to move it beyond academic research, for commercial use. A greater barrier in Deep Learning is recruiting, there is a real scarcity of deep learning scientists. Even the largest technology companies (Google, Microsoft, Apple, etc), are struggling to recruit deep learning scientists, and even recruiting deep learning engineers (who lack a more in-depth scientific understanding of the subject) is an extremely challenging task.

Some people say that publishing has already been disrupted, that this current state is the new model. But I don't think the disruption has even started yet. As Jeff Bezos says, "it's always Day One." In the last ten years, we've seen the rise of digital publishing, print on demand, and the independent author movement, as well as the growth of streaming audio and the use of internet marketing tools like Facebook and Amazon Ads to sell more books. In this episode, I'll talk about some of the possible disruptions to come for authors and the publishing industry due to the rise of Artificial Intelligence (AI) in the next 10 years. This episode is sponsored by my Patrons, authors who are passionate about the future of publishing and help support my time in producing episodes like this. Ten years ago, when I started self-publishing, I was over-the-top excited about the potential of ebooks (see my embarrassing video here!). I could see the incredible possibilities as a creator to reach the whole world with my words. Since starting out in 2008, I have built a multi-six-figure business as an author-entrepreneur, taking action on that feeling of optimism and learning everything I needed to know to write, publish, market, and make a living with my writing.

Overfitting is considered one of the biggest challenges in modern deep learning applications. Conceptually, overfitting occurs when a model generates a hypothesis that is too tailored to a specific dataset to the data making it impossible to adapt to new datasets. A useful analogy to understand overfitting is to think about it as hallucinations in the model. A lot has been written about overfitting singe the early days of machine learning so I won't presume to have any clever ways to explain it. However, I would like to use this post to present three practical ways to think about overfitting in deep learning models.

We are happy to announce the SpeechBrain project, that aims to develop an open-source and all-in-one toolkit based on PyTorch. The goal is to develop a single, flexible, and user-friendly toolkit that can be used to easily develop state-of-the-art speech systems for speech recognition (both end-to-end and HMM-DNN), speaker recognition, speech separation, multi-microphone signal processing (e.g, beamforming), self-supervised learning, and many others. The project will be led by Mila (Montréal) and is sponsored by Samsung, Nvidia, and Dolby. SpeechBrain will also benefit from the collaboration and expertise of other partners such as Avignon Université, Facebook/PyTorch, IBM Research, and Fluent.ai. Reddit is an awesome place to discuss, so please, let us know what you would like to see implemented for the speech community!

PyTorch is a well-designed, flexible, popular, and well-documented toolkit with a very large community. Most speech applications rely on deep learning and signal processing techniques, that can be naturally implemented in PyTorch. Processing steps are performed either on GPUs or CPUs. It is feasible to design end-to-end differentiable systems, where the gradient can potentially flow through all the different parts of the architecture, including parts solving different audio and speech tasks (*e.g. PyTorch is a well-designed, flexible, popular, and well-documented toolkit with a very large community. Most speech applications rely on deep learning and signal processing techniques, that can be naturally implemented in PyTorch.

You might have noticed several artifacts in the Outputs sub-tab. That's because we save a checkpoint at the end of each epoch!

A study by researchers from the University of Rochester found an exodus of artificial intelligence (AI) professors from North American universities to the private sector has reduced the prospect that graduate students will found new AI companies. Those graduates who did start a company usually attracted less venture capital, with the field of deep learning especially affected, according to "Artificial Intelligence, Human Capital, and Innovation," by Michael Gofman and Zhao Jin. This academic attrition could hinder innovation and economic expansion over time, the researchers suggest. The technology industry mostly ignored deep learning's potential until 2010, but interest grew as the Internet produced more data and new computer chips reduced the analytical burden. Large tech companies have hired many academic specialists, including two recent recipients of the ACM A.M. Turing Award honored for their work on neural networks.

Gartner's 2019 CIO Survey found that the number of enterprises implementing AI grew 270 percent in the past four years. That's all impressive, well and good – but how many of those projects have evolved past the POC stage, how many have gone into production and been scaled across the enterprise? AI implementations are up, but Gartner also reported last October that by 2021, more than half of machine learning projects will not be fully deployed because of operational problems. That's the key challenge in enterprise AI today: getting past the project phase, what IBM calls "chapter 2" for AI. Last November, HPE acquired BlueData, maker of container-based software for AI deployment and management.

The new HPE ML Ops solution extends the capabilities of the BlueData EPIC container software platform, providing data science teams with on-demand access to containerized environments for distributed AI / ML and analytics. BlueData was acquired by HPE in November 2018 to bolster its AI, analytics, and container offerings, and complements HPE's Hybrid IT solutions and HPE Pointnext Services for enterprise AI deployments. Enterprise AI adoption has more than doubled in the last four years1, and organizations continue to invest significant time and resources in building machine learning and deep learning models for a wide range of AI use cases such as fraud detection, personalized medicine, and predictive customer analytics. However, the biggest challenge faced by technical professionals is operationalizing ML, also known as the "last mile," to successfully deploy and manage these models, and unlock business value. According to Gartner, by 2021, at least 50 percent of machine learning projects will not be fully deployed due to lack of operationalization.2