Waymo, the self-driving technology company, just came out with the modestly named Content Search, but it could have huge implications for advancing autonomous vehicle technology. Waymo's new Content Search tool allows engineers to catalogue and find billions of images. As explained on its blog, Waymo and Google Research, both divisions of parent company Alphabet, collaborated to create Content Search. By leveraging the search technology similar to what powers Google Photos and Google Image, Waymo engineers can now quickly locate just about any object stored in Waymo's driving history and logs through 20 million miles of collecting data on the road. In essence, the Content Search turns all the objects into a searchable catalogue, accurately tracking billions of images.

AI is the method by which self-driving cars perceive joggers, cyclists, traffic lights, road signs, trees, shrubs, and more, and it informs the way in which they choose to behave when encountered with those signals. The vehicles in Waymo's fleet aren't an exception to the rule -- they tap AI to make real-time driving decisions, in part by matching obstacles spotted by their onboard sensors to the billions of objects in the Alphabet company's database. Large data sets are invaluable in the autonomous driving domain because they enable the underpinning AI to self-improve. But it's been historically tough for engineers to surface samples within those sets without investing time and manual effort. That's why Waymo developed what it calls Content Search, which draws on tech similar to that which powers Google Photos and Google Image Search to let data scientists quickly locate almost any object in Waymo's driving history and logs.

The UK financial services (FS) industry is facing a number of challenges right now and could be said to be in a state of flux. The seemingly never-ending uncertainty over Brexit has been compounded by continued disruption in the market. New competitors and Big Tech brands are reinventing services and using the latest technology to steal market share away from traditional FS providers. Rising customer expectations are also putting pressure on banks and other FS firms to create simpler, more intuitive, and customised products and experiences. Those that fail to respond to these evolving needs risk losing customers, especially now that it's easier than ever for consumers to switch providers.

MySQL is one of the most widely used open source relational database management systems in the world. With a total distribution amounting to more than 100 million worldwide, the software has become the first choice of large data management corporations spanning over a wide range of internet technologies. MySQL was created by a Swedish company MySQL AB in 1995. The developers of the platform were Michael Widenius (Monty), David Axmark and Allan Larsson. The foremost purpose was to provide efficient and reliable data management options to home and professional users.

We all know that, given two events A and B, the probability of the union A U B is given by the formula P(A U B) P(A) P(B) - P( AB) where AB represents the intersection of A and B. Most of us even know that It generalizes to n independent events, and this formula is known as the inclusion-exclusion principle. Let us consider n events A(1), A(2), ..., A(n) where A(k) is for a positive integer number, the property to be divisible by the square of the k-th prime number. We assume here that the first prime number is 2. These events are independent because we are dealing with prime numbers. As n tends to infinity, 1 - P( A(1) U A(2) U ... U A(n)) tends to the probability, for a positive integer number, to be square-free.

There are no shortcuts for data exploration. If you are in a state of mind, that machine learning can sail you away from every data storm, trust me, it won't. After some point of time, you'll realize that you are struggling at improving model's accuracy. In such situation, data exploration techniques will come to your rescue. I can confidently say this, because I've been through such situations, a lot.

We all know that, given two events A and B, the probability of the union A U B is given by the formula P(A U B) P(A) P(B) - P( AB) where AB represents the intersection of A and B. Most of us even know that It generalizes to n independent events, and this formula is known as the inclusion-exclusion principle. Let us consider n events A(1), A(2), ..., A(n) where A(k) is for a positive integer number, the property to be divisible by the square of the k-th prime number. We assume here that the first prime number is 2. These events are independent because we are dealing with prime numbers. As n tends to infinity, 1 - P( A(1) U A(2) U ... U A(n)) tends to the probability, for a positive integer number, to be square-free.

We all know that, given two events A and B, the probability of the union A U B is given by the formula P(A U B) P(A) P(B) - P( AB) where AB represents the intersection of A and B. Most of us even know that It generalizes to n independent events, and this formula is known as the inclusion-exclusion principle. Let us consider n events A(1), A(2), ..., A(n) where A(k) is for a positive integer number, the property to be divisible by the square of the k-th prime number. We assume here that the first prime number is 2. These events are independent because we are dealing with prime numbers. As n tends to infinity, 1 - P( A(1) U A(2) U ... U A(n)) tends to the probability, for a positive integer number, to be square-free.

This article was written by Stephanie Kim. Stephanie has a professional experience with data mining and processing including natural language processing along with a small amount of machine learning and script automation. Do Twitter users who mention Donald Trump swear more than those who mention Hillary Clinton? Let's find out by taking a natural language processing approach (or, NLP for short) to analyzing tweets. This walkthrough will provide a basic introduction to help developers of all background and abilities get started with the NLP microservices available on Algorithmia.

This chart comes from Reddit. Since many data scientists occasionnally have to make a presentation at a conference (or when presenting their doctoral thesis), I thought you would relate to this chart. . Article: What is Data Science? Article: What is Data Science?