f(AI)nder View
December 2018
f(AI)nder View - a monthly digest in the field of
Artificial Intelligence, Machine Learning, Deep Learning
exploring the various levels and faces of AI: from basic to sophisticated ones,
from algorithms and technologies to business applications

Company Nodis - partner of the December issue
TruTint - Transparent, color, fast switching smart glass
Nodis' TruTint smart glass technology is transforming windows, giving people the ability to change the tint, color and temperature characteristics of windows instantly. TruTint is the only color smart glass solution.
Buildings consume 40% of a city's electricity and generate 45% of its CO2 emissions. TruTint smart windows can reduce electricity usage significantly (by up to 40%), enable carbon neutral buildings.
Nodis is headquartered in Singapore and funded by Singapore government (National Research Foundation).
Nodis is Winner of Shell IdeaRefinery, Grand Prize Winner of K-Startup Grand Challenge.
AI ecosystem
AI industry analytics, trends, foresights, insights, challenges, regulations, etc.
The report aggregates a diverse set of metrics, and makes the underlying data easily accessible to the general public.
The AI Index Report is broken into 4 large sections: Volume of Activity, Technical Performance, Derivative Measures, Towards Human Peformance.
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To analyze potential applications for social good, McKinsey Global Institute compiled
a library of about 160 AI social-impact use cases across a range of social domains: Health and hunger, Environment, Infrastructure, Education and others.
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New report "Artificial Intelligence: How knowledge is created, transferred, and used" summarizes research output from key geographies such as Europe, China and US -assessing strengths, weaknesses and characteristics of each.
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One of the world's most famous computer scientists reveals his 'playbook' for bringing AI to every business. The Playbook draws on insights gleaned from leading the Google Brain team and the Baidu AI Group, which played leading roles in transforming both Google and Baidu into great AI companies.
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Over the last 60 or so years, as AI developed, it was heavily influenced by and indeed inspired by neuroscience and psychology. A view some of some directions in which the fields of biological and artificial intelligence may advance hand in hand going forward.
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To What will be the state of AI in 2019?
Artificial Intelligence is the talk of the world and it features prominently in predictions for 2019 (see here and here) and recent surveys by consulting firms and other observers of the tech scene.
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Applications
The research team trained a computer to recognize more than 10,000 transparent glass-like etchings, based on extremely grainy images of those patterns.
The images were taken in very low lighting conditions, with about one photon per pixel — far less light than a camera would register in a dark, sealed room.
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AlphaFold generates the 3D models of proteins far more accurate than any that have come before. Proteins are large, complex molecules essential in sustaining life. Figuring out the 3D shape of a protein purely from its genetic sequence is a complex task that scientists have found challenging for decades.
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Detecting and identifying unreliable pages is of key importance, as it might help to warn users and reduce malicious activity on the platform. Using the top 10 features to determine a Facebook page's reliability,
it is achieved a classification accuracy
of 91.37 percent.
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When you're designing the components that go into your products, why not consider thousands of different designs instead of just three or four? What is Generative Design? How does Generative Design work? What Generative Design means for the Future of Engineering?
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Fingerprint authentication systems are a widely trusted, ubiquitous form of biometric authentication, deployed on billions of smartphones and other devices worldwide. Yet a new study reveals a surprising level of vulnerability in these systems.
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AI system is directly controlling data centre cooling, while remaining under the expert supervision of data centre operators.
This first-of-its-kind cloud-based control system is now safely delivering energy savings in multiple Google data centres
with consistent energy savings of around
30 percent on average.
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Introductions
The full evaluation of AlphaZero describes how AlphaZero quickly learns each game to become the strongest player in history for each, despite starting its training from random play, with no in-built domain knowledge but the basic rules of the game.
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BERT is a model that broke several records for how well models can handle language-based tasks. The team also open-sourced the code of the model, and made available for download versions of the model that were already pre-trained on massive datasets.
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Here is discussed: six core elements,
six important mechanisms, and twelve applications, including games, robotics, NLP, computer vision, finance, business management, healthcare, education, energy, transportation, computer systems, science, engineering, and art.
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How to analyse an audio/music signal in Python and then utilise the skills learnt to classify music clips into different genres. Music Genre Classification is one of the many branches of Music Information Retrieval. From here you can perform other tasks on musical data like beat tracking, music generation, recommender systems, track separation, instrument recognition.
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We can define an evolution strategy as an algorithm that provides the user a set of candidate solutions to evaluate a problem. The evaluation is based on an objective function that takes a given solution and returns a single fitness value. The iterative process will stop once the best known solution is satisfactory for the user.
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A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities. Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. Because evolution is an algorithmic process, evolution's creativity is not limited to nature.
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Toolbox
Physics simulation dovetails with AI, robotics and computer vision, self-driving vehicles, and high performance computing. The PhysX SDK is a scalable multi-platform game physics solution supporting a wide range of devices, from smartphones to high-end multicore CPUs and GPUs.
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Generating long pieces of music is a challenging problem, as music contains structure at multiple timescales, from milisecond timings to motifs to phrases to repetition of entire sections.
Music Transformer, an attention-based neural network that can generate music with improved long-term coherence.
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Look at the developments in some of the key areas (Natural Language Processing, Computer Vision, Reinforcement Learning, etc.) in Artificial Intelligence from a data science practitioners' perspective.
What were these breakthroughs?
What happened in 2018 and what can be expected in 2019?
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In recent years, innovative Generative Adversarial Networks have demonstrated a remarkable ability to create nearly photorealistic images. GAN Paint demo and GAN Dissection method provide evidence that the networks have learned some aspects of composition. The GANpaint app works by directly activating and deactivating sets of neurons in a deep network trained to generate images.
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UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology. The result is a practical scalable algorithm that applies to real world data. UMAP has no computational restrictions on embedding dimension, making it viable as a general purpose dimension reduction technique for machine learning.
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The NVIDIA paper proposes an alternative generator architecture for GAN that draws insights from style transfer techniques.
The system can learn and separate different aspects of an image unsupervised; and enables intuitive, scale-specific control of the synthesis. The new generator architecture has achieved state-of-the-art performance in face generation.
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AI hardware
The 56-page paper has eight chapters covering topics ranging from key attributes of AI chips to technology challenges, architecture design trends, and emerging computing technologies.
The paper mainly discusses three types of AI chips: GPU, TPU and neuromorphic chips.
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MLPerf - a broad ML benchmark suite for measuring performance of ML software frameworks, ML hardware accelerators, and ML cloud platforms. The benchmark suite released their first round of results that measure the speed of major machine learning (ML) hardware platforms, including Google TPUs, Intel CPUs, and NVIDIA GPUs.
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Committee of experts assembled by the U.S. National Academies of Science, Engineering and Medicine had been grappling with the question:
When will quantum computers mature into something of practical commercial value?
Its 205-page report is released to the public.
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The AI chip unicorn that's about to revolutionize everything has computational Graph at its Core. Graphcore is now officially a unicorn, with a valuation of $1.7 billion. What exactly Graphcore does and how that is different from the competition?
What is so special about Graphcore's own architecture?
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The company has released a Jetson AGX Xavier Module that gives robots and other intelligent machines the processing oomph they need for their AI 'brains.' You're not about to buy one yourself -- it costs $1,099 each in batches of 1,000 units. The Jetson Xavier system-on-chip at the heart of the module relies on no less than six processors to get its work done.
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What's the best type of device from which to build a neural network? Of course, it should be fast, small, consume little power, have the ability to reliably store many bits-worth of information. Researchers tried out several new devices to get closer to the ideal needed for deep learning and neuromorphic computing.
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Courses
Andrew Ng: "AI is not only for engineers. This non-technical course will help you understand AI technologies and spot opportunities to apply AI to problems in your own organization. You will see examples of what today's AI can – and cannot – do. This course is intended for everyone, ranging from CEOs, product managers, marketers, salespeople, designers, to financiers."
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Artificial General Intelligence from MIT
The course is a series of lectures and conversations that explore the nature of human and machine intelligence from a variety perspectives.
Among the lecturers: Ray Kurzweil, Max Tegmark, Stephen Wolfram, Josh Tenenbaum, Guido van Rossum, Vladimir Vapnik, Ilya Sutskever and others.
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18 long lecture videos from DeepMind.
One part is on machine learning with deep neural networks, the other part is about prediction and control using reinforcement learning. Possible applications areas to be discussed include object recognition, natural language processing, learning to play classic board games as well as video games.
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Events
8-11 January, 2019. Las Vegas, USA.
With 170,000+ attendees viewing 3,900 exhibitors, it's not surprising that there's specializations in AI, drones and robotics.
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22-23 January, 2019. Singapore.
Discover the Emerging Technologies
that will Change the World. EmTech is co-organised by MIT Technology Review.
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RE•WORK Deep Learning Summit 2019
24-25 January, 2019. San Francisco, USA.
100 speakers, 900 attendess, 10 stages
AI Assistant, Industry Applications, Investors & Startups, Futurescaping and others.
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f(AI)nder View - a monthly digest in the field of
Artificial Intelligence, Machine Learning, Deep Learning
exploring the various levels and faces of AI:
from basic to sophisticated ones,
from algorithms and technologies to business applications

f(AI)nder View is prepared by f(AI)nder Lab
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