Collaborating Authors

Why You need Math for Machine Learning


A while back, I was on Twitter and I saw the following exceptional take on Twitter. This is a very popular idea online, and I had meant to write about this sooner. But thanks to all the insanity happening in the ML research domain, I got side-tracked. However, now that I have the time I can finally cover this in-depth. In this post, I will cover "Why you absolutely need Math for Machine Learning."

My questions about your data


One of the points I've been stressing for a long time now is that: It's not about data It's about business, the business outcomes, about the value that is generated for business. Business is the driver, data and what it produces is the enabler. As any other corporate asset, data's purpose is to generate business value. Organizations have apprehended the importance of data in their businesses and are looking deeper into data to gain a competitive advantage, implementing machine learning and artificial intelligence to achieve new business objectives and to move ahead of competitors in the industry. A data asset is every piece of data that organizations use to generate revenues, they are currently among its the most valuable assets, and organizations must invest seriously on managing these assets.

'AI can predict outcomes, but not exercise judgment'


NO matter how intelligent machines will be, the human element is still needed when it comes to decisions involving law and judgments. But in the long run, using artificial intelligence (AI) in our justice system will help improve the quality of judgements and avoid lengthy and expensive litigation processes. While Sabah and Sarawak are using it now in courts, plans are still in the pipeline for the system to be applied in Peninsular Malaysia. "AI is used to assist us in better decision- making, as it amplifies our capacity and detects flaws at the same time. "However, it also has no element of emotion or compassion which can only come from a person.

Superintelligence: Paths, Dangers, Strategies: Bostrom, Nick: 9780198739838: Books


Nick Bostrom is a Swedish-born philosopher and polymath with a background in theoretical physics, computational neuroscience, logic, and artificial intelligence, as well as philosophy. He is a Professor at Oxford University, where he leads the Future of Humanity Institute as its founding director. He is the author of some 200 publications, including Anthropic Bias (2002), Global Catastrophic Risks (2008), Human Enhancement (2009), and Superintelligence: Paths, Dangers, Strategies (2014), a New York Times bestseller which helped spark a global conversation about artificial intelligence. Bostrom's widely influential work, which traverses philosophy, science, ethics, and technology, has illuminated the links between our present actions and long-term global outcomes, thereby casting a new light on the human condition. He is recipient of a Eugene R. Gannon Award, and has been listed on Foreign Policy's Top 100 Global Thinkers list twice.



OneOrigin is an Enterprise Software and Technology Solutions company recognized for innovation, design, and development of cutting edge AI products and...

Text classification for online conversations with machine learning on AWS


Online conversations are ubiquitous in modern life, spanning industries from video games to telecommunications. This has led to an exponential growth in the amount of online conversation data, which has helped in the development of state-of-the-art natural language processing (NLP) systems like chatbots and natural language generation (NLG) models. Over time, various NLP techniques for text analysis have also evolved. This necessitates the requirement for a fully managed service that can be integrated into applications using API calls without the need for extensive machine learning (ML) expertise. AWS offers pre-trained AWS AI services like Amazon Comprehend, which can effectively handle NLP use cases involving classification, text summarization, entity recognition, and more to gather insights from text.

In letter to board, Enthusiast leadership asks CEO to step down

Washington Post - Technology News

On June 7, Greywood announced it intended to nominate Shinggo Lu, a current Enthusiast employee and the co-founder of U.GG, a "League of Legends" analytics platform and a recent Enthusiast acquisition, to the new board. Lu shared the news in an Enthusiast Slack channel with over 250 employees, entreating other employees to ask him questions, and sparking a spirited but largely cordial conversation between staff and some members of the company's leadership over Enthusiast's direction and treatment of employees, according to messages viewed by The Post.

Google, Nvidia split top marks in MLPerf AI training benchmark


MLCommons director David Kanter made the point that improvements in both hardware architectures and deep learning software have led to performance improvements on AI that are ten times what would be expected from traditional chip scaling improvements alone. Google and Nvidia split the top scores for the twice-yearly benchmark test of artificial intelligence program training, according to data released Wednesday by the MLCommons, the industry consortium that oversees a popular test of machine learning performance, MLPerf. The version 2.0 round of MLPerf training results showed Google taking the top scores in terms of lowest amount of time to train a neural network on four tasks for commercially available systems: image recognition, object detection, one test for small and one for large images, and the BERT natural language processing model. Nvidia took the top honors for the other four of the eight tests, for its commercially available systems: image segmentation, speech recognition, recommendation systems, and solving the reinforcement learning task of playing Go on the "mini Go" dataset. Also: Benchmark test of AI's performance, MLPerf, continues to gain adherents Both companies had high scores for multiple benchmark tests, however, Google did not report results for commercially available systems for the other four tests, only for those four it won. Nvidia reported results for all eight of the tests.

Marcus Invest by Goldman Sachs just became much more accessible to investors


Goldman Sachs announced that beginning Wednesday, June 29, it is lowering its account minimums and management fees for Marcus Investing accounts. The account minimum is being reduced drastically, from $1,000 to $0, and the minimum investment is now just $5. In addition, the company is reducing its portfolio management fee from 0.35% to 0.25%. These are welcomed changes to the robo-advisory investment platform. A robo-advisor investment platform manages a selection of portfolios on behalf of investors.

'Crossy Road' creator Andy Sum's next game will arrive on July 20th


Publisher No More Robots has announced that the next game from Crossy Road creator Andy Sum will be released on Steam on July 20th. While Crossy Road is a hit arcade-style title in the vein of Frogger, TombStar takes its cue from bullet hell roguelikes, such as Enter the Gungeon and The Binding of Isaac. TombStar, which was announced in 2020, is a colorful space Western top-down shooter. There are three characters to choose from, each with their own playstyle. You'll pick up abilities, weapons and perks to aid you in battle against the Grimheart Gang.