machine learning


Will AI Change Leadership?

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If you're a CEO, you're being watched. A little more than you usually are, anyway. Research led by two Harvard Business School professors is attempting to find keys to the CEO's success through close study not of the exec's decisions or of others' opinions but of what they say and how they look when they say it. Using video interviews with 130 leaders, the researchers applied machine-learning tools to scrutinize the words that CEOs chose, how much they tended to stray from topic to topic, the positivity or negativity of the words they used, and their facial expressions. The era of machine learning has provided a boost for that last task.


The time is now for Machine Learning

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Some commentators think machine learning is too new a field to be deployed for ad fraud mitigation. In the last few years, expertise and technological developments have come a long way in the field of machine learning. Widely used in the cybersecurity space already, ad tech seems to only just be catching on to the value of machine learning in ad fraud prevention. Did you know the term machine learning dates back to the 1950s? So how is it that a topic older than the compact cassette tape, is one of the hottest topics in technology today?


How AI can save the retail industry

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The future of retail continues looking grim, as more brick and mortar stores close their doors. US retailers have announced 8,558 store closures so far this year, with total US store closures predicted to hit 12,000 by the end of 2019, reported Coresight Research on Friday. While the internet and automation are typically to blame for these closures, the same technology could actually be the solution for physical store locations, said Paul Winsor, general manager of retail at DataRobot. "If retailers want to stay open in the existing stores that they are operating in, my recommendation to them is to ask: Are they understanding the changing habits of those customers, and how they're shopping with them, in those locations?" "To survive in the tough, tough retail market, you have to start to turn your business, and make predictions, based on learning from your historical data," he added.


How AI powered LG ThinQ points to a more advanced future smart home

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Personalised and proactive interactions are at the centre of what LG is building with its AI powered LG ThinQ ecosystem, under the strategy of "Evolve, Connect and Open", realised on the IFA showfloor in the always on, ambient intelligence of the LG ThinQ Home. Think a smart home full of connected products that know what you need before you do, with the tech to act on it, thanks to algorithms that are capable of analysing individual habits. The challenge: to transform these predictions into useful features that make for comfortable living. "LG ThinQ draws on data analytics and machine learning algorithms from LG's proprietary AI chip and LG Neural Engine to better understand the user's situation, patterns and preferences," said Dr IP Park, president and chief technology officer for LG Electronics. "Learning from the user's behavior, LG ThinQ products will ultimately evolve to anticipate the user's needs and deliver heightened performance."


Meet Five Synthetic Biology Companies Using AI To Engineer Biology

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AI is changing the field of synthetic biology and how we engineer biology. It's helping engineers design new ways to design genetic circuits -- and it could leave a remarkable impact on the future of humanity TVs and radios blare that "artificial intelligence is coming", and it will take your job and beat you at chess. But AI is already here, and it can beat you -- and the world's best -- at chess. In 2012, it was also used by Google to identify cats in YouTube videos. Today, it's the reason Teslas have Autopilot and Netflix and Spotify seem to "read your mind."


Experiment #102 How to Create an Azure Notebooks Project and Deploy a Summarization Service - idiwork

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In this new post, we will learn step by step how to create an Azure Notebook Project for our Experiment #102 and implement a text summary service by writing some scripts in Python and running them with Jupyter. First, we can access this Azure Notebooks Service by visiting https://notebooks.azure.com/ Before creating our own functions, we must load a bundle of Python libraries that includes Azure Machine Learning SDK (azureml-sdk), ONNX Runtime (onnxruntime) and the Natural Language Toolkit (nltk). The "init" file or Python script looks like that: After that, we need to upload the file to our Azure Notebook project. Then, we can go inside, see the code and run it from Jupyter to load all the libraries.


Use object recognition to make sure your car hasn't been stolen

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My recommendation is to get video examples from throughout the week, with different lighting, different'scenarios' on the street such as cars, bins, animals, ghosts, whatever else is common. In my case, I need video with and without those bins. Now comes the fun part. Go ahead and download this nifty tool from Machine Box called Objectbox. Follow the instructions to get yourself setup with the annotation tool in Objectbox, and place your videos into the boxdata/files directory.



The minimum viable data set

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Very often in the context of AI, it is mentioned that enormous amounts of data are required in order to work with it in the first place. Very complex models have to be programmed and the success of a project is often associated with many unpredictabilities and risks. However, as a general rule, this is completely wrong. This article is all about giving you a perspective on how to handle situations of data scarcity and the possibilities to consider in this context. Of course, there are complex projects that place extreme demands on the amount of data in order to achieve effective results, but usually this has to do with poor planning or a deliberately high willingness to experiment.