Genotype imputation enhances genetic data by predicting missing SNPs using reference haplotype information. Traditional methods leverage linkage disequilibrium (LD) to infer untyped SNP genotypes, relying on the similarity of LD structures between genotyped target sets and fully sequenced reference panels. Recently, reference-free deep learning-based methods have emerged, offering a promising alternative by predicting missing genotypes without external databases, thereby enhancing privacy and accessibility. However, these methods often produce models with tens of millions of parameters, leading to challenges such as the need for substantial computational resources to train and inefficiency for client-sided deployment. Our study addresses these limitations by introducing a baseline for a novel genotype imputation pipeline that supports client-sided imputation models generalizable across any genotyping chip and genomic region. This approach enhances patient privacy by performing imputation directly on edge devices. As a case study, we focus on PRS313, a polygenic risk score comprising 313 SNPs used for breast cancer risk prediction. Utilizing consumer genetic panels such as 23andMe, our model democratizes access to personalized genetic insights by allowing 23andMe users to obtain their PRS313 score. We demonstrate that simple linear regression can significantly improve the accuracy of PRS313 scores when calculated using SNPs imputed from consumer gene panels, such as 23andMe. Our linear regression model achieved an R^2 of 0.86, compared to 0.33 without imputation and 0.28 with simple imputation (substituting missing SNPs with the minor allele frequency). These findings suggest that popular SNP analysis libraries could benefit from integrating linear regression models for genotype imputation, providing a viable and light-weight alternative to reference based imputation.

Earlier this year, the technology company OpenAI released a program called DALL-E 2, which uses artificial intelligence to transform text into visual art. People enter prompts ("plasticine nerd working on a 1980s computer") and the software returns images that showcase humanlike vision and execution, veer into the bizarre, and might even tease creativity. The results were good enough for Cosmopolitan, which published the first-ever AI-generated magazine cover in June--an image of an astronaut swaggering over the surface of Mars--and they were good enough for the Colorado State Fair, which awarded an AI artwork first place in a fine-art competition. OpenAI gave more and more people access to its program, and those who remained locked out turned to alternatives like Craiyon and Midjourney. Soon, AI artwork seemed to be everywhere, and people started to worry about its impacts. Trained on hundreds of millions of image-text pairs, these programs' technical details are opaque to the general public--more black boxes in a tech ecosystem that's full of them.

Adding unique, quality images can be a great help for SEO. Often, when you're writing an article, it's hard to find the right image to illustrate it – especially if you're looking for a royalty-free image. This is where quality images can make all the difference, as a captivating image can help grab the attention of internet users and improve your article's search rankings. Optimizing your images is a good SEO practice. It notably helps to strengthen your semantic power via keywords and ensures your presence in Google images.

Machine Learning (ML) is one of those heavily used buzzwords that you often hear these days. Most managers want to use it but don't know where to start or even what it actually means. It may seem mysterious, technical, and intimidating at first. But in this post, I'll breakdown what ML is, its applications, how ML is built, and the skills you need to develop ML at a very high "management" level. In most simple words, Machine Learning is the ability of certain types of computer programs to learn from experience in much the same way as we humans learn from our experiences.

When you think of a lawyer's office, you typically visualize a cabin with row of books stacked up from top to bottom. Now imagine if they didn't have to read them either! ROSS Intelligence, an artificial intelligence based legal research software that gives answers to questions put to it in plain English. For instance, one could type in "is a non-compete clause legal in India? What is the position of the Bombay High Court?" and have Ross generate a response backed up with references including relevant judgments and readings.

Traditional first-order logic (FOL) reasoning systems usually rely on manual heuristics for proof guidance. We propose TRAIL: a system that learns to perform proof guidance using reinforcement learning. A key design principle of our system is that it is general enough to allow transfer to problems in different domains that do not share the same vocabulary of the training set. To do so, we developed a novel representation of the internal state of a prover in terms of clauses and inference actions, and a novel neural-based attention mechanism to learn interactions between clauses. We demonstrate that this approach enables the system to generalize from training to test data across domains with different vocabularies, suggesting that the neural architecture in TRAIL is well suited for representing and processing of logical formalisms.

Sony has made Aibo much harder to resist for people who love chocolate-colored dogs. The tech giant has launched a tricolor version of its robotic canine with two shades of brown, and it's now available for pre-order in Japan. Since it's pretty much just a recolored release with no differences in hardware and software, it also costs 198,000 JPY (US$1,800) like the original Aibo, not including taxes and subscription fees. Sony promises to roll out a new security feature now that it has teamed up with security firm Secom, though. According to Engadget Japanese, the company will offer a 1,480 yen-a-month security feature in Japan starting in June, which will take advantage the robot's capabilities to recognize faces and to create indoor maps. The new service will give Aibo the capability to patrol the house and make sure all family members are safe and sound.

Understanding interpretations Establishing what happens inside the "brain" of a neural network has been an ongoing research aim for Google over the past few years. The company first described the internal workings of neural networks in a 2015 paper, explaining how the systems are able to create new images and recognise items. The company has now followed up on its "Inceptionism" paper with a new study into the "Building Blocks of Interpretability." Over the past year, Google has acquired more understanding of the way in which neural networks interpret images. In a blog post, the company said it's now exploring how to understand neural networks in the context of the "bigger picture."

For the Winter '17 cohort in collaboration with Macquarie Group, over 800 companies were vetted and 10 were accepted. The selected scaleups cover a range of technology verticals applicable to all enterprise. Collectively, the scaleups have raised £40m to date, have between 10 and 40 employees each and have an incredible breadth of enterprise clients -- ranging from Airbnb to Aston Martin to Barclays to Compass Group to L'Oreal to Wells Fargo. Collecting leads at events is a broken process. We're here to fix it.

The gold rush for artificial intelligence (AI) is officially in full swing. Big players like Google and Facebook and small teams alike are in an all-out sprint toward the goal of creating the next generation of AI assistants that will fundamentally change how we live and work. I am in awe at the pace of progress, because every week it feels like a new barrier is breached, a tool grows more robust, or a new startup is launched with the ability to transform an industry. However, the most surprising observation continues to be people's underestimation of AI. Specifically how the general population seems so unable, or unwilling, to imagine that a machine could ever match a human's ability in any job -- particularly their own.