Collaborating Authors


Method is all you need: 7 mistakes to avoid in Data Science


Once upon a time, data science was valuable only for a handful of Big Tech companies. Data science is now revolutionizing many "traditional" sectors: from automotive to finance, from real estate to energy. Research by PwC estimates that AI will contribute over 15.7 trillion US dollars to the global GDP by 2030 -- for reference, the GDP of the Eurozone in 2018 was worth 16 trillion dollars [1]. All businesses now perceive their data as assets and the insights they can gain as a competitive advantage. Yet, more than 80% of all data science project fails [2]. Each failed project fails for its own peculiar reasons, but, in three years of experience, we noticed some patterns.

Versioning Machine Learning Experiments vs Tracking Them


When working on a machine learning project it is common to run numerous experiments in search of a combination of an algorithm, parameters and data preprocessing steps that would yield the best model for the task at hand. To keep track of these experiments Data Scientists used to log them into Excel sheets due to a lack of a better option. However, being mostly manual, this approach had its downsides. To name a few, it was error-prone, inconvenient, slow, and completely detached from the actual experiments. Luckily, over the last few years experiment tracking has come a long way and we have seen a number of tools appear on the market that improve the way experiments can be tracked, e.g.

How AI is helping Humans Fight Climate Change


The impact of climate change is on a global scale and ranges from drastic shifts in the weather patterns that jeopardize food production to rising levels of the sea that increase the chances of floods. The contribution of artificial intelligence in solving the problem of climate change is significant. AI can be deployed to construct more energy-efficient infrastructures, designing low-carbon substances, greener transportation, and strict monitoring of deforestation. AI is helping humans fight climate change in many different ways. AI has the potential to save our planet from anticipated dangers.



The graph represents a network of 1,765 Twitter users whose tweets in the requested range contained "HealthIT", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 07 December 2021 at 19:14 UTC. The requested start date was Tuesday, 07 December 2021 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 13-day, 23-hour, 43-minute period from Tuesday, 23 November 2021 at 01:16 UTC to Tuesday, 07 December 2021 at 01:00 UTC.

A Non-technical Leader's Guide to AI Success


There's a reason why most AI/ML projects are not successful. Leaders -- you should stop treating AI as another IT initiative. AI is inherently different and it requires leaders to change their thinking, culture and approach. The last decade was about convincing the world that AI works. This decade is about using AI responsibly and profitably.

Artificial intelligence carries a huge upside. But potential harms need to be managed


Artificial intelligence and machine learning have the potential to contribute to the resolution of some of the most intractable problems of our time. Examples include climate change and pandemics. But they have the capacity to cause harm too. And they can, if not used properly, perpetuate historical injustices and structural inequalities. To mitigate against their potential harms, the world needs frameworks for the governance of data that are economically enabling and that preserve rights.

Machines that see the world more like humans do


Computer vision systems sometimes make inferences about a scene that fly in the face of common sense. For example, if a robot were processing a scene of a dinner table, it might completely ignore a bowl that is visible to any human observer, estimate that a plate is floating above the table, or misperceive a fork to be penetrating a bowl rather than leaning against it. Move that computer vision system to a self-driving car and the stakes become much higher -- for example, such systems have failed to detect emergency vehicles and pedestrians crossing the street. To overcome these errors, MIT researchers have developed a framework that helps machines see the world more like humans do. Their new artificial intelligence system for analyzing scenes learns to perceive real-world objects from just a few images, and perceives scenes in terms of these learned objects. The researchers built the framework using probabilistic programming, an AI approach that enables the system to cross-check detected objects against input data, to see if the images recorded from a camera are a likely match to any candidate scene.

Olaf Scholz: Germany's Staid But Steady Next Chancellor

International Business Times

Often described as austere and even robotic, Social Democrat Olaf Scholz nonetheless managed to inspire German voters in this year's election with a campaign that played on his reputation as a safe pair of hands. Scholz, 63, will now take office as Germany's ninth post-war chancellor, replacing Angela Merkel who is leaving the political stage after 16 years. The Social Democrats (SPD) had begun the election campaign at rock bottom in the polls, with many completely writing off Scholz's chances of heading the next government -- so much so that he didn't even have an official biography until this week. But Scholz managed to stage a stunning upset, beating Merkel's conservatives by positioning himself as the best candidate to continue her legacy, even adopting her famous "rhombus" hand gesture on a magazine cover. Unlike his rivals, he also managed not to make embarrassing mistakes during a campaign that drew on his reputation as a quiet workhorse, using the slogan "Scholz will sort it".

Junior Machine Learning Software Engineer at JPMorgan Chase Bank, N.A.


The Corporate & Investment Banking Production Management Artificial Intelligence Operations has a mission to change how we support/manage the environment by leveraging new technology like AI/ML. The team applies AI to solve open ended problems that align state of the art AI solutions with enterprise scale challenges. In doing so, the team builds software systems, AI models, technology process and intelligent frameworks that minimize the technology risk, increase operational efficiency and increase the investment efficacy in general. In this particular instance, we are looking for a Data Scientist to join the team whose mission is to combine advanced analytical and quantitative techniques with technology business acumen to serve the technology portfolio of solutions. As a junior level Machine Learning Software Engineer in the Corporate & Investment Bank, you will be responsible for modeling complex problems, discover insights, manipulate terabytes of data and build cutting edge hybrid AI products that solve high impact and big scale problems through statistical modeling, machine learning, visualization and story telling that increases the operational value of our technology portfolio.

How AI Changed -- in a Very Big Way -- Around the Year 2000


In "Hyping Artificial Intelligence Hinders Innovation" (podcast episode 163), Andrew McDiarmid interviewed Erik J. Larson, author of The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do (2021) (Harvard University Press, 2021) on the way "Machines will RULE!" Erik Larson has founded two two DARPA-funded artificial intelligence startups. Inthe book he urges us to go back to the drawing board with AI research and development. This portion begins at 01:59 min. A partial transcript and notes, Show Notes, and Additional Resources follow.