How Edge AI Chipsets Will Make AI Tasks More Efficient

AI Chip

Artificial intelligence (AI) is an innovation powerhouse. It autonomously learns on its own and evolves to meet simple and complex needs, from product recommendations to business predictions. As more people and services produce data, more powerful AI is necessary to process it all. AI chipsets that use edge computing are the solution.

Read More

Principal Component Analysis (PCA)

This is the first post in a two-part series on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Although they have similarities (such as their names), they each achieve different tasks. In this post, I will give describe what PCA is, how it works, and as an example use it to define an S&P 500 index fund. Example code and other related resources can be found in the last section of this post.

Read More

How to encode Time-Series into Images for Financial Forecasting using Convolutional Neural Networks

Within forecasting there’s an age old question, ‘is what I am looking at a trend?’ Within the realm of statistics there are many tools that, with various degrees of success, answer said question. Yet, no approach has been able to achieve that which started the field of data forecasting in the first place. Looking at a graph derived from the data and drawing conclusions from it. However, thanks to Deep Learning and Artificial Neural Networks that is about to change. Welcome Computer Vision!

Read More

The dark side of Data Science

As I discuss in my article “Myths of Modelling: Data Speak”, Positivism — and, by association, its mythical beliefs — had been pretty thoroughly discredited by the 1960s. Unfortunately, as if often the case in the history of ideas, the counter-revolution over-compensated. Where the early revolutionaries would loosen the chains of narrow empiricism and open up for a more enlightened dialogue between hypotheses and the data that inspire and regulate them, the next generation would throw empiricism out all together. In the ensuing vacuity of common sense, practitioners had little choice but to crawl back to frameworks steeped in positivism.

Read More

A case for AI regulation

AI Regulation

Who will benefit from advances in artificial intelligence? And should we be worried? Advances in artificial intelligence (AI) are likely to be of most benefit to three groups: the wealthy, those who have specialist skills in jobs that are not easily automated, and those who can work effectively with intelligent machines. These groups of people represent a minority that will have a marked advantage in the future over the rest of humanity. However, advances in AI could benefit everyone while still bolstering inequality.

Read More

Splice gets $55 million for its software bringing beats from bedrooms to bandstands

Steve Martocci Splice

Splice, the New York-based, AI-infused, beat-making software service for music producers created by the founder of GroupMe, has managed to sample another $55 million in financing from investors for its wildly popular service.

Read More

Artificial Intelligence for Automated and Autonomous discovery of better battery materials

AI Autonomous Discovery

While applications such as electric mobility, stationary storage, drones and medical implants continue to take off, the global demand for sustainable rechargeable batteries is expected to increase drastically in the next decade. Europe alone would need a cell production capacity of at least 200 GWh up to the TWh range.

Read More

Dell’s US$50m R&D centre in Singapore to drive innovation in edge computing

Dell Singapore

Dell opens global Innovation Facility in Singapore, the First Outside US, , to focus on R&D for edge computing, data analytics and augmented reality. American tech giant Dell Technologies officially launched a US$50 million (S$66 million) research and development centre here on Monday (Feb 22) that will drive innovation in computing near where data is located.

Read More

Natural Language Processing-Based Quantification of the Mental State of Psychiatric Patients

ML Psychiatry

Psychiatric practice routinely uses semistructured and/or unstructured free text to record the behavior and mental state of patients. Many of these data are unstructured, lack standardization, and are difficult to use for analysis. Thus, it is difficult to quantitatively analyze a patient’s illness trajectory over time and his or her responsiveness to treatment, and it is also difficult to compare different patients quantitatively. In this article, experts in the field of psychiatry, along with machine learning models, have …

Read More

Quickstart Guide to Auto-Sklearn (AutoML) for Machine Learning Practitioners

Using AutoML frameworks in the real world is becoming a regular thing for machine learning practitioners. People often ask: does automated machine learning (AutoML) replace data scientists? Not really. If you’re eager to find out what AutoML is and how it works, join me in this article. I’m going to show you auto-sklearn, a state-of-the-art and open-source AutoML framework.

Read More