Collaborating Authors


The AI-Powered Micro-Business with Ash Fontana


Artificial Intelligence is already part of our lives in the tools and services we use every day. As AI development accelerates, how can authors and small businesses use it as leverage to expand income and opportunities? Ash Fontana gives some ideas in this interview on The AI-First Company.

The robot-dog Spot lay off by the New York Police Department


Spot can carry loads weighing up to 14 kg on its back. It stands up when it falls and works in temperatures between -20 and 45 degrees Celsius. It has a range of 90 minutes and a maximum speed of 4.8 km/h. The robot can move according to the instructions given to it, avoid obstacles and maintain its balance in extreme circumstances. It is also water resistant, making it a fairly versatile work tool.

Someone please put these classic science fiction novel stamps on my wall


In fact, I can't remember the last time I used one (sorry, Mum). But the latest batch from the Royal Mail has me wanting to send letters to every corner of the universe. The British postal service has released a wondrous new collection of artworks that will be featured on its tiny postage stamps, celebrating six classic science fiction novels by British writers. Set to mark the 75th anniversary of HG Wells' death and the 70th anniversary of John Wyndham's classic novel The Day of the Triffids being published, the collection features illustrations for Frankenstein by Mary Shelley, The Time Machine by HG Wells, Brave New World by Aldous Huxley, Childhood's End by Arthur C Clarke, Shikasta by Doris Lessing, and of course, The Day of the Triffids. The artists behind the works are Sabina Šinko, Francisco Rodríguez, Thomas Danthony, Mick Brownfield, Matt Murphy, and Sarah Jones.

Optimal Approximation Rate of ReLU Networks in terms of Width and Depth Machine Learning

This paper concentrates on the approximation power of deep feed-forward neural networks in terms of width and depth. It is proved by construction that ReLU networks with width $\mathcal{O}\big(\max\{d\lfloor N^{1/d}\rfloor,\, N+2\}\big)$ and depth $\mathcal{O}(L)$ can approximate a H\"older continuous function on $[0,1]^d$ with an approximation rate $\mathcal{O}\big(\lambda\sqrt{d} (N^2L^2\ln N)^{-\alpha/d}\big)$, where $\alpha\in (0,1]$ and $\lambda>0$ are H\"older order and constant, respectively. Such a rate is optimal up to a constant in terms of width and depth separately, while existing results are only nearly optimal without the logarithmic factor in the approximation rate. More generally, for an arbitrary continuous function $f$ on $[0,1]^d$, the approximation rate becomes $\mathcal{O}\big(\,\sqrt{d}\,\omega_f\big( (N^2L^2\ln N)^{-1/d}\big)\,\big)$, where $\omega_f(\cdot)$ is the modulus of continuity. We also extend our analysis to any continuous function $f$ on a bounded set. Particularly, if ReLU networks with depth $31$ and width $\mathcal{O}(N)$ are used to approximate one-dimensional Lipschitz continuous functions on $[0,1]$ with a Lipschitz constant $\lambda>0$, the approximation rate in terms of the total number of parameters, $W=\mathcal{O}(N^2)$, becomes $\mathcal{O}(\tfrac{\lambda}{W\ln W})$, which has not been discovered in the literature for fixed-depth ReLU networks.

IA Planner: Motion Planning Using Instantaneous Analysis for Autonomous Vehicle in the Dense Dynamic Scenarios on Highways Artificial Intelligence

In dense and dynamic scenarios, planning a safe and comfortable trajectory is full of challenges when traffic participants are driving at high speed. The classic graph search and sampling methods first perform path planning and then configure the corresponding speed, which lacks a strategy to deal with the high-speed obstacles. Decoupling optimization methods perform motion planning in the S-L and S-T domains respectively. These methods require a large free configuration space to plan the lane change trajectory. In dense dynamic scenes, it is easy to cause the failure of trajectory planning and be cut in by others, causing slow driving speed and bring safety hazards. We analyze the collision relationship in the spatio-temporal domain, and propose an instantaneous analysis model which only analyzes the collision relationship at the same time. In the model, the collision-free constraints in 3D spatio-temporal domain is projected to the 2D space domain to remove redundant constraints and reduce computational complexity. Experimental results show that our method can plan a safe and comfortable lane-changing trajectory in dense dynamic scenarios. At the same time, it improves traffic efficiency and increases ride comfort.

Product Management Implications of AI for IoT


Having surpassed peak marketing hype, Internet of Things (IoT) technologies are now an established building block, both in the business environment and in industrial applications. An illustration of technology maturity is how combinations with other technologies enable new operational solutions. Gone are the days of deploying exploratory pilots. Now, product managers focus less on connectivity and more on packaging connectivity, data management, and security for operational use. This involves integrating cloud infrastructure services, AI and ML analytics, and visualization dashboards to design end-to-end IoT systems with operational users in mind.

Using NLP to improve your Resume - KDnuggets


Now you can read an overall summary of the job role and your existing Resume! Did you miss anything about the job role that is being highlighted in summary? Small nuanced details can help you sell yourself. Does your summarized document make sense and bring out your essential qualities? Perhaps a concise summary alone is not sufficient. Next, let us measure how similar your Resume is to a job specification. Figure 2 provides the code. Broadly we make a list of our text objects then create an instance of the sklearn CountVectorizer() class.

S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration Artificial Intelligence

Previous studies dominantly target at self-supervised learning on real-valued networks and have achieved many promising results. However, on the more challenging binary neural networks (BNNs), this task has not yet been fully explored in the community. In this paper, we focus on this more difficult scenario: learning networks where both weights and activations are binary, meanwhile, without any human annotated labels. We observe that the commonly used contrastive objective is not satisfying on BNNs for competitive accuracy, since the backbone network contains relatively limited capacity and representation ability. Hence instead of directly applying existing self-supervised methods, which cause a severe decline in performance, we present a novel guided learning paradigm from real-valued to distill binary networks on the final prediction distribution, to minimize the loss and obtain desirable accuracy. Our proposed method can boost the simple contrastive learning baseline by an absolute gain of 5.5~15% on BNNs. We further reveal that it is difficult for BNNs to recover the similar predictive distributions as real-valued models when training without labels. Thus, how to calibrate them is key to address the degradation in performance. Extensive experiments are conducted on the large-scale ImageNet and downstream datasets. Our method achieves substantial improvement over the simple contrastive learning baseline, and is even comparable to many mainstream supervised BNN methods. Code will be made available.

A radish in a tutu walking a dog? This AI can draw it really well


An artist can draw a baby daikon radish wearing a tutu and walking a dog, even if they've never seen one before. But this kind of visual mashup has long been a trickier task for computers. Now, a new artificial-intelligence model can create such images with clarity -- and cuteness. This week nonprofit research company OpenAI released DALL-E, which can generate a slew of impressive-looking, often surrealistic images from written prompts such as "an armchair in the shape of an avocado" or "a painting of a capybara sitting in a field at sunrise." (And yes, the name DALL-E is a portmanteau referencing surrealist artist Salvador Dalí and animated sci-fi film "WALL-E.") A new AI model from OpenAI, DALL-E, can create pictures from the text prompt "an illustration of a baby daikon radish in a tutu walking a dog".

OpenAI's DALL-E app generates images from just a description


OpenAI, the company co-founded by Elon Musk and backed by Microsoft, has already mastered Dota 2 and the art of writing fake news. Now, it has reached another milestone with DALL-E (a portmanteau of "Wall-E" and "Dali"), an AI app that can create an image out of nearly any description. For example, if you ask for "a cat made of sushi" or a "high quality illustration of a giraffe turtle chimera," it will deliver those things, often with startlingly good quality (and sometimes not). DALL-E can create images based on a description of its attributes, like "a pentagonal green clock," or "a collection of glasses is sitting on a table." In the latter example, it places both drinking and eye glasses on a table with varying degrees of success.