The gap between the promise of Artificial Intelligence (AI) and its implementation in practice has never been greater than it was in 2020. There were clearly some major milestone AI achievements last year. Take Google DeepMind's AlphaFold, which was shown to accurately predict 3D models of protein structures, paving the way for groundbreaking research across every field of biology. Or in June, when a beta version of GTP3 was publicly released by Microsoft - an incredibly sophisticated model capable of almost any language task, including writing in the style of Chaucer, and even basic coding. Yet outside of these tech giants, AI adoption remains in exploratory stages for the vast majority of enterprises and a long way off becoming an integral part of day to day business. At this point in the AI adoption cycle, many enterprises hold an untenable position as long as they fail to appreciate the enormous potential for embedding machine learning into their products and business processes.

Data Science has many branches of specialisation but among the popular ones, the major branches which can be clearly defined are Data Analytics, Machine Learning, Deep Learning and Artificial Intelligence. Yet, these branches are often mixed with one another so I thought of clarifying some of the thin differences between these topics. Data Analytics is making sense of the data. In one sentence, describing the data and deriving predictions and conclusions from them to take better business decisions. Data analysis describes the current condition for an organization by translating data into information understandable to the business officials. Hence, data analytics requires constant improvement in methods for data collection, analysis, and reporting.

To be a real "full-stack" data scientist, or what many bloggers and employers call a "unicorn," you've to master every step of the data science process -- all the way from storing your data, to putting your finished product (typically a predictive model) in production. But the bulk of data science training focuses on machine/deep learning techniques; data management knowledge is often treated as an afterthought. Data science students usually learn modeling skills with processed and cleaned data in text files stored on their laptop, ignoring how the data sausage is made. Students often don't realize that in industry settings, getting the raw data from various sources to be ready for modeling is usually 80% of the work. And because enterprise projects usually involve a massive amount of data that their local machine is not equipped to handle, the entire modeling process often takes place in the cloud, with most of the applications and databases hosted on servers in data centers elsewhere.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. In their decades-long chase to create artificial intelligence, computer scientists have designed and developed all kinds of complicated mechanisms and technologies to replicate vision, language, reasoning, motor skills, and other abilities associated with intelligent life. While these efforts have resulted in AI systems that can efficiently solve specific problems in limited environments, they fall short of developing the kind of general intelligence seen in humans and animals. In a new paper submitted to the peer-reviewed Artificial Intelligence journal, scientists at UK-based AI lab DeepMind argue that intelligence and its associated abilities will emerge not from formulating and solving complicated problems but by sticking to a simple but powerful principle: reward maximization. Titled "Reward is Enough," the paper, which is still in pre-proof as of this writing, draws inspiration from studying the evolution of natural intelligence as well as drawing lessons from recent achievements in artificial intelligence.

At this year's International Conference on Learning Representations (ICLR), a team of researchers from the University of Maryland presented an attack technique meant to slow down deep learning models that have been optimized for fast and sensitive operations. The attack, aptly named DeepSloth, targets "adaptive deep neural networks," a range of deep learning architectures that cut down computations to speed up processing. Recent years have seen growing interest in the security of machine learning and deep learning, and there are numerous papers and techniques on hacking and defending neural networks. But one thing made DeepSloth particularly interesting: The researchers at the University of Maryland were presenting a vulnerability in a technique they themselves had developed two years earlier. In some ways, the story of DeepSloth illustrates the challenges that the machine learning community faces.

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In this article, we will review our recent work titled "Simulated Data Generation Through Algorithmic Force Coefficient Estimation for AI-Based Robotic Projectile Launch Modeling" by Sajiv Shah, Ayaan Haque, and Fei Liu. In this paper, we present FCE-NN, a novel method of modeling robotic launching of non-rigid objects using neural networks which are trained with supplemental simulated data, generated from algorithmic force coefficient estimation. This work has been accepted to ACIRS 2021. The paper is available in ArXiv, and the project website is here. We will first introduce our motivation and problem, then review the method, and finally present some short results and final thoughts.

Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Deep Learning, … Those are quite some terms! As a matter of fact, with so many terms, it almost feels like being trapped in a jungle of buzzwords when entering the adventure to find the most beneficial data-driven use cases in a company. A nice rule-of-thumb to get an overview in this complex jungle is to state that for modern AI applications, Supervised Learning (SL) is an integral part in most cases. SL is a subset of AI, in which models -- such as deep neural networks -- learn by examples. We will take a look at how SL works in this blog post to better understand why creating such learning examples is one of the key tasks in implementing modern AI.

Whether you want to build a complex deep learning model for a self-driving car, a live face recognition program, or making your image processing software for your graduate project, you will have to learn OpenCV along the way. OpenCV is a huge image and video processing library designed to work with many languages such as python, C/C, Java, and more. It is the foundation for many of the applications you know that deal with image processing. Getting started with OpenCV can be challenging, primarily if you rely on its official documentation, which is known for being cumbersome and hard to understand. Attend the tech festival of the year and get your super early bird ticket now!

Evaluating the inherent difficulty of a given data-driven classification problem is important for establishing absolute benchmarks and evaluating progress in the field. To this end, a natural quantity to consider is the \emph{Bayes error}, which measures the optimal classification error theoretically achievable for a given data distribution. While generally an intractable quantity, we show that we can compute the exact Bayes error of generative models learned using normalizing flows. Our technique relies on a fundamental result, which states that the Bayes error is invariant under invertible transformation. Therefore, we can compute the exact Bayes error of the learned flow models by computing it for Gaussian base distributions, which can be done efficiently using Holmes-Diaconis-Ross integration. Moreover, we show that by varying the temperature of the learned flow models, we can generate synthetic datasets that closely resemble standard benchmark datasets, but with almost any desired Bayes error. We use our approach to conduct a thorough investigation of state-of-the-art classification models, and find that in some -- but not all -- cases, these models are capable of obtaining accuracy very near optimal. Finally, we use our method to evaluate the intrinsic "hardness" of standard benchmark datasets, and classes within those datasets.