The amount of companies hiring out Data Scientists thinking that they're going to come in, wave a wand and solve all problems with AI is appalling. This is not good for the industry, the companies, or the Data Scientists. Disrupting an industry takes a lot more than hiring a couple of Data Scientists and handing them a wishlist. Solving real problems with Artificial Intelligence requires experience across several technical domains that start with the hardware and ends with the production model,

Interesting take on adopting Quantum Computing in 2021, but I still think that there is a lot to overcome before most companies can actually use Quantum Computing for problems that really matter. Most people don't know, but Quantum Computers are a thing already, the problem is that they're not as powerful right now as we need it to be, and it is very hard to program them and get good results out of it. More on this topic here: https://www.analyticsinsight.net/if-not-then-organizations-should

Photography is an industry hard to disrupt these days. With phones that are ever more powerful, carrying multiple lenses, and connected to our multitude of apps, it has become increasingly hard to find a reason to buy a DSLR camera. Alice might be an exception, though. It is small, portable, connects to your phone, and is incredibly powerful. And yes... it uses Artificial Intelligence to compensate for our lack of photography skills. Here is a video describing it: Here is an article about

Just read a very interesting article on an intelligent material that re-arranges itself in response to a learning stimulus. This is very exciting, and I think, the way to go when it comes to embedding Artificial Intelligence to all things around us. It also shows how the concept of Artificial Intelligence is a lot more encompassing than the usual definitions can accommodate. Here is a cool article explaining how it works: https://scitechdaily.com/the-first-steps-toward-a-quantum-brain-an-inte

Have you tried any distributed learning algorithms? If you are just starting out in this area, I have my doubts, but if you have been on this path for a few years, you might have faced one of those models. The incredible development of the machine learning area in the last decade has not only brought a new state of the art to several problems but has also taken processing optimization and parallelization to another level. With increasingly larger models, any common machine or even a single sup

Even though Reproducibility in Machine Learning is a theme that people hear about now and then, we still see that people are practicing it only to a certain degree. Even between Kaggle [https://www.kaggle.com/] competition winners, we still see a lot of hard-to-reproduce code in Notebooks. Our goal here is to outline some reproducibility elements and how we tackled them in a recent competition. First, what reproducibility stands for in Machine Learning? During a Machine Learning project, we hav

Suppose you are a Data Scientist or Machine Learning Engineer (or another role name of this kind). You took your time to analyze your dataset, clean it, and prepare it to train your model. You then prepared many model candidates using the most recent techniques and took your time to fine-tune them. After all this extensive work, you finally created a model to be proud of. You finally finished your job. Well, unfortunately, not. If your model never goes live and is actively used, delivering value

There are major infrastructural challenges in running large scale data centers. One is providing sufficient electric power to keep the facility running. A datacenter running tens of thousands of servers consumes roughly 10 Megawatts of power. Servers not only consume vast amounts of energy, they also generate a lot of heat. The air inside a data center will become sweltering unless you cool it down. Servers cannot function reliably in high temperatures. The cooling solution needs to be both hi

Soon we might live in a world where one can never be sure that video and voice recording is real, no matter how realistic it looks and sounds. Deep learning methods are used with artificial neural networks to create what is known as deepfakes – visual and audio content that, to the naked eye, looks absolutely real. The potential uses of deepfakes are limited only by the imagination of people who have access to the technology required to manufacture them. As technology advances, the tools for cr

Human brain consumes incredibly low amounts of energy, has great longevity, and requires little maintenance. No man-made computing hardware comes even close to a human brain in these qualities. Right now, the brain is mostly a black box to us. We know verry little about how it works. As of yet, scientists have had a limited toolset to study the inner workings of a human brain. Devices that interface with the brain could help us better understand it, repair, and possibly improve it. The most pr

The advances of technology in our world have continued to increase and self-driving cars are the next logical step for our society. This poses a fundamental question: do we know how safe autonomous vehicles can be? SAE International is an organization who describes a categorization for "levels of driving automation". It defines six levels of automation for cars, ranging from Level 0 (no driving automation) to Level 5 (full automation), transitioning gradually from "driver support features" to "

With the constant advancements of genetic engineering, a common concern is to be able to identify the lab-of-origin of genetically engineered DNA sequences. For that reason, AltLabs has hosted the Genetic Engineering Attribution Challenge to gather many teams to propose new tools to solve this problem. Here we show our proposed method that aims to rank the most likely labs-of-origin and generate embeddings for DNA sequences and labs. These embeddings can also be used to perform various other tas