The basic metric of success is to get the error less than the baseline. It means that the final model has good quality. The first question is how to measure quality. It could be squared errors. After that, we can estimate the interval by bootstrapping method.

Our Geneia Data Intelligence Lab (GDI) prioritizes projects that address major cost drivers and enable our clients – health plans, physicians, hospitals and employers – to identify, stratify and accurately predict high-cost claimants and conditions such as heart failure and diabetes. Want to use #AI to lower #healthcosts? Get @Geneia's newest white paper to learn how.

We've created MuseNet, a deep neural network that can generate 4-minute musical compositions with 10 different instruments, and can combine styles from country to Mozart to the Beatles. MuseNet was not explicitly programmed with our understanding of music, but instead discovered patterns of harmony, rhythm, and style by learning to predict the next token in hundreds of thousands of MIDI files. MuseNet uses the same general-purpose unsupervised technology as GPT-2, a large-scale transformer model trained to predict the next token in a sequence, whether audio or text. Since MuseNet knows many different styles, we can blend generations in novel ways[1]. Here the model is given the first 6 notes of a Chopin Nocturne, but is asked to generate a piece in a pop style with piano, drums, bass, and guitar.

Let's face it--presentations can be stressful. You have a goal and you want to be successful, and that's when the fear, stress, and discomfort becomes real. Will your presentation help you land your message? Can you deliver the presentation effectively and in a way that maximizes your success? These are all real emotions that many people face when tasked with a making a presentation.

Algorithms'learn' from the data they are trained on to create a machine learning model that can perform a given task effectively without needing specific instructions, such as making predictions or accurately classifying images and emails. These techniques are already used widely, for example to identify spam emails, diagnose diseases from X-rays, predict crop yields and will soon drive our cars. While the technology holds enormous potential to positively transform our world, artificial intelligence and machine learning are vulnerable to adversarial attacks, a technique employed to fool machine learning models through the input of malicious data causing them to malfunction. Dr Richard Nock, machine learning group leader at CSIRO's Data61 said that by adding a layer of noise (i.e. an adversary) over an image, attackers can deceive machine learning models into misclassifying the image. "Adversarial attacks have proven capable of tricking a machine learning model into incorrectly labelling a traffic stop sign as speed sign, which could have disastrous effects in the real world. "Our new techniques prevent adversarial attacks using a process similar to vaccination," Dr Nock said. "We implement a weak version of an adversary, such as small modifications or distortion to a collection of images, to create a more'difficult' training data set.

Amazon is considering launching drones that patrol neighbourhoods and could even call the police if they spot something amiss, according to a patent. The company may set up a subscription service for worried homeowners that means its delivery aircrafts fly overhead looking for broken windows, graffiti or a fire. Its drones will be able to take photos or record videos - sparking fears they could be used to collect data Big Brother-style. In an apparent attempt to quell such fears, the patent states drone footage will obscure adjacent properties. It will also require proof of ownership of the object or property being monitored, as well as permission from others living nearby, for example in an apartment block.

AI is getting smarter, requiring less training data and moving from cloud to Edge. Finnish AI startups gathered last week in Business Finland's Customer Club to share relevant information for intelligent industry and to check the latest state of the art of AI solutions for industrial use. There are plenty of small, young Finnish companies that have created money saving and innovative AI solutions especially for pulp and paper industry, mining companies and oil refineries that are strong businesses in Finland. Possibilities for different profitable applications are numerous with solutions that combine the use of cloud and edge in storing and analyzing data. All data from industrial machines cannot be moved to the cloud because there is typically just too much data or the latency requirements don't allow it.