The Chinese search behemoth, Baidu, announced a $200m investment initiative focused on AI and ran a pre-release of their new open source deep learning framework called PaddlePaddle. The company has a ways to go to compete given that developers are still more likely to use TensorFlow, which holds the lead along with Caffe, Keras and Theano. Speaking of the growing number of hardware and software configurations available today, this research paper provides helpful benchmarks. 

Backchannel run a rare piece on how Apple uses machine learning. It states that a 200mb software package runs on the iPhone encompassing “app usage data, interactions with contacts, neural net processing, a speech modeler and a natural language event modeling system”. I’ve held the view for a while now that today’s AI techniques and infrastructure will re-open a class of historically intractable problems while also enabling us to rethink how products and features should be designed. Apple seem to think the same: “Machine learning is enabling us to say yes to some things that in past years we would have said no to. It’s becoming embedded in the process of deciding the products we’re going to do next.”

Salesforce announced their internal umbrella AI initiative, modestly called Einstein, which will go on to power many of the company’s cloud services, as well as expose AI tools to end users. The team of 175 data scientists includes talent from acquired startups MetaMind, PredictionIO and RelateIQ. The company’s flagship event, Dreamforce, will attract 170k people into SF next week.

Six of the most powerful technology companies have set up the Partnership on AI as a non-profit aimed at advancing public understanding of AI and formulate best practices on the challenges and opportunities within the field. An important catalyst to this end will undoubtedly be the continuation of open source technology development, which Seldon’s founder articulates in this piece.

Stanford’s 100 year study on AI published their first report. It finds “no cause for concern that AI is an imminent threat to humankind. No machines with self-sustaining long-term goals and intent have been developed, nor are they likely to be developed in the near future”. From a public policy perspective, it recommends to:

Princeton postdoc, Aaron Bornstein, writes a fascinating piece on the interpretability of AI systems. Given that deep learning models create internal representations of input data using features that weren’t handcrafted, their inner works are difficult (but not entirely impossible) to understand. Without interpretability, high-stakes use cases (e.g. healthcare, government, finance) just won’t adopt models that otherwise deliver state of the art performance. On the same subject, this piece runs through several initiatives focused on verification, including a new DARPA project on Explainable AI.

a16z’s Chris Dixon sets out 11 reasons to be excited about the future of technology with short soundbites for each. Five of these are either directly related to or will be enabled by AI and machine learning.

UC Berkeley announced a new Center for Human-Compatible AI to study how AI used for mission-critical tasks act in a way that is aligned with human values. One enabling technique is inverse reinforcement learning, where an agent (e.g. robot) can learn a task by observing human actions instead of learning to optimise a task on its own.

Designer Ines Montani makes the case for how front-end development can improve AI. Music to my ears! I take the view that although AI can be used to solve fascinatingly complex problems, wrapping a service with an API for others to dream up the most powerful use case isn’t the path to building a valuable company. Instead, one should productise technology with user-centered design as a top priority. Ines walks through how design can “improve the collection of annotated data, communicate the capabilities of the technology to key stakeholders and explore the system’s behaviours and errors.”

Google has published a high-level description of their deep learning-based recommendation system for YouTube using TensorFlow. The system uses two networks, one to generate potential candidates from the corpus of videos and a second to rank these candidates using video features, user history and context. In contrast to many deep learning models, the ranking models uses hundreds of engineering features because the raw data doesn’t lend itself well as a direct input. Two weeks later, the company open sourced a data set of 8 million YouTube video URLs along with labels from a set of 4,800 classes.

Spotify takes us through the evolution of their machine learning teams who drive the recommendations behind their Discovery Weekly and Radio products.

Bloomberg run a piece on how 12 hedge funds that use machine learning-based quantitative strategies performed above an index of all hedge funds this year. On the a similar note, Numerai, the distributed quant hedge fund, explain their thesis on ensembling machine intelligence from thousands of data scientists around the world to achieve breakthroughs in stock market prediction accuracy.

Ten years after the original release of Google Translate, the Google Brain team announce a new state of the art Neural Machine Translation System (paper here). The system takes the entire text to be translated as an input to a recurrent neural network instead of breaking the input sentence into words and phrases. The network pays attention to a weighted distribution over the encoded input vector (i.e. Chinese word) most relevant to generate output word (i.e. English word). Of note, the Chinese to English Google Translate service is 100% machine translation based, producing 18 million translations per day!

Google DeepMind announced a research partnership with the Radiotherapy Department at University College London Hospitals NHS Foundation Trust. The project focuses on improving the process of segmenting normal tissue from cancer in the head and neck region so that radiotherapy causes less collateral damage to non-cancer regions.

The Next Platform track research publications in deep learning since the summer and find a particular emphasis on medical applications for prenatal ultrasound, breast mammography, brain cancer and melanoma.

Slightly more left field, Elon Musk announced that he’s made progress on a design for a neural lace. This would would effectively serve as an interface between our brains and a machine to avoid the benign situation that humans become “house cats” in the age of superintelligent AI.

Stratechery opines a piece on Google, Uber and the evolution of transportation as a service. He makes the case that Uber, which is rolling out their self-driving fleet in Pittsburgh, is in pole position in a race that Travis Kalanick’s calls “existential”.

Fortune run a piece on the journey of Justin.tv founders from building a live streaming business to a self-driving car technology company, both of which sold for over $1bn.

The US Federal Government released its first rulebook on autonomous vehicles, including regulation on the safe testing and deployment of AVs (including data sharing) as well as a model US state policy framework to regulate AVs.

Mapillary, the Swedish company operating a crowdsourced street level imagery service joined UC Berkeley’s Deep Drive where it will focus on semantic segmentation of real-world imagery and structure from motion to help drive research in deep learning and computer vision for autonomy.

The majority of machine learning models we talk about in the real world are discriminative insofar as they model the dependence of an unobserved variable y on an observed variable x to predict y from x. As such, they are used for supervised classification or regression tasks. Generative models, on the other hand, are fully probabilistic models of all variables from which randomly generated observable data points can be obtained. They’re all the rage at the moment because they have been shown to synthesise artificial content (text, images, video, sound) that look and sound real from a small, unlabeled datasets. Here’s a range of research and resources that help us understand how they work, why they’re fascinating and use cases:

Attention and Augmented Recurrent Neural Networks, Google Brain. In this piece, the authors describe how RNNs are useful for modelling sequences of data and explore four extensions thereof. The piece includes many visualisations to help distill the important bits.

Show and Tell: image captioning open sourced in TensorFlow, Google Brain. This system involves classifying a target image and using these encodings to initialise an image encoder with a vision model for the image captioning system to produce a descriptive caption. In this work, the authors add a fine-tuning step during which the captioning system is improved by jointly training its vision and language components on human generated captions. Interestingly, the new model implemented in TensorFlow can generate novel natural-sounding English descriptions of scenes that weren’t included in the training data. Paper here.

Stealing machine learning models via prediction APIs, EPFL. With the rise of cloud-based ML as a service offerings, the security of training data and predictions moves front and center. This work investigates model extraction attacks that seek to duplicate the functionality of a model by reverse engineering its parameters. The authors demonstrate that successful attacks rely on the outputs of prediction APIs, namely the high-precision confidence values and class labels, to iteratively identify the model parameters. They show that services built by Google, Amazon, Microsoft and BigML are particularly concerned

The big deal: Movidius, the European company that created computational image-processor chips and software for connected devices, sold to Intel for $400m. The company was founded in 2005 and raised $95m over 7 rounds from Draper Esprit, Atlantic Bridge Capital, Robert Bosch Venture and others. According to Intel, Movidius’ algorithms tuned for deep learning, navigation and mapping, and natural interactions “will be deployed across Intel’s efforts on augmented, virtual and merged reality (AR/VR/MR), drones, robotics, digital security cameras and beyond.”

61 companies raised $305m over 62 financing rounds from 112 investors. Median deal size was $2m (down from $2.8m in last issue) at a pre-money valuation of $7.7m (up from $6.3m in last issue). Deals include:

For a roundup of the European landscape for AI companies, check out this research piece by Project Juno AI.

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