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Accelerating the Practical Use of AI - InformationWeek


The hype for how artificial intelligence can miraculously change the world continues to fill media outlets. Still, the reality of how rapidly the science behind AI is evolving and becoming mainstream in every industry and facet of business will not be impeded. By the year 2025, the intersection of "advanced" AI and intelligent machines will become a part of every user's "things I just know how to use." As more industries adopt AI solutions and become savvy about how AI impacts their engagement with suppliers and employees, it is important for organizations to follow four key steps to implement it. While roles like data scientist, chief data officer, and senior data engineer are vital to implementing AI/ML systems, the two following roles are imperative for practical implementation.

Google: Learn cloud skills for free with our new training tracks


Google is offering a free course for people who are on the hunt for skills to use containers, big data and machine-learning models in Google Cloud. The initial batch of courses consists of four tracks aimed at data analysts, cloud architects, data scientists and machine-learning engineers. The January 2021 course offers a fast track to understand key tools for engineers and architects to use in Google Cloud. It includes a series on getting started in Google Cloud, another focussing on its BigQuery data warehouse, one that delves into the Kubernetes engine for managing containers, another for the Anthos application management platform, and a final chapter on Google's standard interfaces for natural language processing and computer vision AI. Participants need to sign up to Google's "skills challenge" and will be given 30 days' free access to Google Cloud labs.

The Robots are Coming: Is AI the Future of Biotech?


AI, or artificial intelligence, has taken root in biotech. In this article, we explore its newfound niches in the industry. Artificial intelligence (AI) and machine learning (ML) have become ubiquitous in tech startups, fueled largely by the increasing availability and amount of data and cheaper, more powerful computers. Now, if you are a new tech startup, ML or AI capabilities represent your minimum ticket to enter the industry. Over the past few years, AI and ML have started to peek their heads into the realm of biotech, due to an analogous transformation of biotech data.

Top Data Science Education Initiatives By Institutions In 2020


While normal education suffered a standstill in 2020, there were a lot of online courses and programs that were initiated by some of the most prestigious institutions as well as big tech giants so that the process of learning and skill development doesn't suffer. As the trend has been for a few years now, some of the most interesting initiatives were seen in the field of data science. In this article, we have listed some of the prominent data science education programs and initiatives in 2020. Microsoft, in collaboration with Netflix, has launched three new learning modules on beginners concepts in data science, along with machine learning and artificial intelligence. The design of these courses is inspired by the Netflix original film -- 'Over The Moon,' where a young girl Fei Fei, who builds a rocket to the moon, embarks on a mission to prove the existence of Moon Goddess.

15 Free Data Science, Machine Learning & Statistics eBooks for 2021 - KDnuggets


An Introduction to Statistical Learning, with Applications in R (ISLR) can be considered a less advanced treatment of the topics found in another classic of the genre written by some of the same authors, The Elements of Statistical Learning. Another major difference between these 2 titles, beyond the level of depth of the material covered, is that ISLR introduces these topics alongside practical implementations in a programming language, in this case R.

Top 100 Artificial Intelligence Companies in the World


Artificial Intelligence (AI) is not just a buzzword, but a crucial part of the technology landscape. AI is changing every industry and business function, which results in increased interest in its applications, subdomains and related fields. This makes AI companies the top leaders driving the technology swift. AI helps us to optimise and automate crucial business processes, gather essential data and transform the world, one step at a time. From Google and Amazon to Apple and Microsoft, every major tech company is dedicating resources to breakthroughs in artificial intelligence. As big enterprises are busy acquiring or merging with other emerging inventions, small AI companies are also working hard to develop their own intelligent technology and services. By leveraging artificial intelligence, organizations get an innovative edge in the digital age. AI consults are also working to provide companies with expertise that can help them grow. In this digital era, AI is also a significant place for investment. AI companies are constantly developing the latest products to provide the simplest solutions. Henceforth, Analytics Insight brings you the list of top 100 AI companies that are leading the technology drive towards a better tomorrow. AEye develops advanced vision hardware, software, and algorithms that act as the eyes and visual cortex of autonomous vehicles. AEye is an artificial perception pioneer and creator of iDAR, a new form of intelligent data collection that acts as the eyes and visual cortex of autonomous vehicles. Since its demonstration of its solid state LiDAR scanner in 2013, AEye has pioneered breakthroughs in intelligent sensing. Their mission was to acquire the most information with the fewest ones and zeros. This would allow AEye to drive the automotive industry into the next realm of autonomy. Algorithmia invented the AI Layer.

Estimating mixed-memberships using the Symmetric Laplacian Inverse Matrix Machine Learning

Community detection has been well studied in network analysis, and one popular technique is spectral clustering which is fast and statistically analyzable for detect-ing clusters for given networks. But the more realistic case of mixed membership community detection remains a challenge. In this paper, we propose a new spectral clustering method Mixed-SLIM for mixed membership community detection. Mixed-SLIM is designed based on the symmetrized Laplacian inverse matrix (SLIM) (Jing et al. 2021) under the degree-corrected mixed membership (DCMM) model. We show that this algorithm and its regularized version Mixed-SLIM {\tau} are asymptotically consistent under mild conditions. Meanwhile, we provide Mixed-SLIM appro and its regularized version Mixed-SLIM {\tau}appro by approximating the SLIM matrix when dealing with large networks in practice. These four Mixed-SLIM methods outperform state-of-art methods in simulations and substantial empirical datasets for both community detection and mixed membership community detection problems.

Learning Graphons via Structured Gromov-Wasserstein Barycenters Machine Learning

We propose a novel and principled method to learn a nonparametric graph model called graphon, which is defined in an infinite-dimensional space and represents arbitrary-size graphs. Based on the weak regularity lemma from the theory of graphons, we leverage a step function to approximate a graphon. We show that the cut distance of graphons can be relaxed to the Gromov-Wasserstein distance of their step functions. Accordingly, given a set of graphs generated by an underlying graphon, we learn the corresponding step function as the Gromov-Wasserstein barycenter of the given graphs. Furthermore, we develop several enhancements and extensions of the basic algorithm, $e.g.$, the smoothed Gromov-Wasserstein barycenter for guaranteeing the continuity of the learned graphons and the mixed Gromov-Wasserstein barycenters for learning multiple structured graphons. The proposed approach overcomes drawbacks of prior state-of-the-art methods, and outperforms them on both synthetic and real-world data. The code is available at

Are we Forgetting about Compositional Optimisers in Bayesian Optimisation? Machine Learning

Bayesian optimisation presents a sample-efficient methodology for global optimisation. Within this framework, a crucial performance-determining subroutine is the maximisation of the acquisition function, a task complicated by the fact that acquisition functions tend to be non-convex and thus nontrivial to optimise. In this paper, we undertake a comprehensive empirical study of approaches to maximise the acquisition function. Additionally, by deriving novel, yet mathematically equivalent, compositional forms for popular acquisition functions, we recast the maximisation task as a compositional optimisation problem, allowing us to benefit from the extensive literature in this field. We highlight the empirical advantages of the compositional approach to acquisition function maximisation across 3958 individual experiments comprising synthetic optimisation tasks as well as tasks from Bayesmark. Given the generality of the acquisition function maximisation subroutine, we posit that the adoption of compositional optimisers has the potential to yield performance improvements across all domains in which Bayesian optimisation is currently being applied.

How to use Machine Learning for Anomaly Detection and Conditional Monitoring - KDnuggets


Before doing any data analysis, the need to find out any outliers in a dataset arises. These outliers are known as anomalies. This article explains the goals of anomaly detection and outlines the approaches used to solve specific use cases for anomaly detection and condition monitoring. The main goal of Anomaly Detection analysis is to identify the observations that do not adhere to general patterns considered as normal behavior. For instance, Figure 1 shows anomalies in the classification and regression problems.