How AI can plug the $40bn patent black hole Sifted

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Companies looking for ways to cut costs as they brace for a coronavirus-induced economic slowdown should consider their patent portfolio. It's like a cupboard in desperate need of a spring clean. Businesses spend over $40bn on maintaining their patent portfolio each year, according to a new study from the UK intellectual property (IP) startup Aistemos and media platform IAM, but less than 20% of companies believe they have the right portfolio. Large companies hold tens of thousands of patents aimed at protecting the company's business from copying and legal issues. Around 4.5% of a company's revenues, on average, are vulnerable to patent litigation, according to the US consultancy Analysis Group.


Towards an ImageNet Moment for Speech-to-Text

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Speech-to-text (STT), also known as automated-speech-recognition (ASR), has a long history and has made amazing progress over the past decade. Currently, it is often believed that only large corporations like Google, Facebook, or Baidu (or local state-backed monopolies for the Russian language) can provide deployable "in-the-wild" solutions. Following the success and the democratization (the so-called "ImageNet moment", i.e. the reduction of hardware requirements, time-to-market and minimal dataset sizes to produce deployable products) of computer vision, it is logical to hope that other branches of Machine Learning (ML) will follow suit. The only questions are, when will it happen and what are the necessary conditions for it to happen? If the above conditions are satisfied, one can develop new useful applications with reasonable costs. Also democratization occurs - one no longer has to rely on giant companies such as Google as the only source of truth in the industry.


Last Week in AI

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Every week, Invector Labs publishes a newsletter that covers the most recent developments in AI research and technology. You can find this week's issue below. You can sign up for it below. Training is one of the frequently overlooked elements of building machine learning solutions at scale. While training machine learning models seems relatively simple conceptually, it gets really complicated when applied to large models or to a large number of models.


Responding to COVID-19 with AI and machine learning

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Today I published a perspective paper on COVID-19. The paper is co-authored with members of the Cambridge Centre for AI in Medicine (which I recently founded and I am directing), and calls on governments and healthcare authorities to use proven AI and machine learning techniques and existing data to coordinate a response to the disease. If you'd like to ask me about the paper or discuss it further, please leave a question/comment below, and I'll get back to you. I've also provided a link to the full paper at the bottom of this post. Both the UK and the international community are still in the early stages of a crisis that will see an unbelievable amount of pressure put on social and healthcare infrastructure.


[FREE] Complete Machine Learning Course: Go from zero to hero Udemy Coupon

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Go From Beginner To Advanced In Machine Learning. Welcome to my course "Complete Machine Learning Course: Go from zero to hero". By using this comprehensive course you will go from beginner to advanced. In this course i will assume that you are a complete beginner and by the end of that course you will be at intermediate level. This course contain Real World examples and hands on practicals without neglecting the basics.


ELLIS against Covid-19

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The world is facing a public health emergency caused by the Covid-19 pandemic. We all need to take this on, and science can make a major contribution. This online workshop will present projects on how to tackle Covid-19 using methods of machine learning and AI, carried out by leading international researchers. Research topics include outbreak prediction, epidemiological modelling, drug development, viral and host genome sequencing, and health care management. As many are interested in the topic or eager to contribute, the event will be open to the general public via livestreaming and recording.


The 2020 Decade for Workers: Disruption Is the Only Constant

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The next 10 years look just as topsy-turvy. Artificial intelligence and machine learning promise to change the competitive landscape for many companies. At the same time, talented professionals will continue to demand more from their jobs through increased calls for transparency around pay and fairness and more flexibility in work-life balance. It's a lot for companies to navigate, and they're struggling with it: an analysis by Korn Ferry of more than 150,000 leadership profiles shows that only 15% of business leaders today have the right blend of skills to be the leaders of tomorrow. But such disruption can be a boon to workers who are agile and forward thinking.


Creating Your Own AI Chatbot Has Become Easy and Here Are the Steps to Do It - TechWalls

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Disclosure: This is a sponsored post, we were compensated to publish this article on our website. Chatbots are becoming commercially available to everyone. At the same time, their numbers keep growing as their applications are becoming more realistic each day. One of the reasons for this is that people in need of chatbots even have the option to create their own, without any programming language. Thanks to platforms like SnatchBot and their Chatbot Creator, it is now possible for everyone to build their own chatbot even with the latest features like artificial intelligence.


Python Programming for Beginners in Data Science

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Data Science, Machine Learning, Deep Learning & AI are hot areas right now. But to learn these, for some of us programming is a bit of a problem. Not all of us are from a programming background. Or some come from a Java background and might not know Python. These days, Python is the de-facto ( almost) programming language for Data Science.


How artificial intelligence can transform influencer marketing

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Influencer marketing is the newest breakthrough to have shaken up the digital marketing landscape. Once an experimental channel, it has grown exponentially over the past few years – up to $6.5 billion in 2019. Now a mainstay of marketing organizations, the key success factor is figuring out how to get the most from it. Artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are revolutionising the way brands conduct influencer marketing. AI-powered influencer marketing tech is helping brands in three key areas: identifying the right creators, suggesting impactful workflow actions, and creating more relevant content.