Machine Learning. The New AI

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Such an encoding can naturally implement a classification scheme where input features are encoded in the spike times of their corresponding input neurons, while the output class is encoded by the output neuron that spikes earliest. The Ihmehimmeli project team holding a himmeli , a symbol for the aim to build recurrent neural network architectures with temporal encoding of information.

We recently published and open-sourced a model in which we demonstrated the computational capabilities of fully connected spiking networks that operate using temporal coding.

Our model uses a biologically-inspired synaptic transfer function , where the electric potential on the membrane of a neuron rises and gradually decays over time in response to an incoming signal, until there is a spike. The strength of the associated change is controlled by the "weight" of the connection, which represents the synapse efficiency. Crucially, this formulation allows exact derivatives of postsynaptic spike times with respect to presynaptic spike times and weights.

The process of training the network consists of adjusting the weights between neurons, which in turn leads to adjusted spike times across the network. Much like in conventional artificial neural networks, this was done using backpropagation. We used synchronization pulses, whose timing is also learned with backpropagation, to provide a temporal reference to the network. We trained the network on classic machine learning benchmarks, with features encoded in time. The spiking network successfully learned to solve noisy Boolean logic problems and achieved a test accuracy of However, unlike conventional networks, our spiking network uses an encoding that is in general more biologically-plausible, and, for a small trade-off in accuracy, can compute the result in a highly energy-efficient manner, as detailed below.


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While training the spiking network on MNIST, we observed the neural network spontaneously shift between two operating regimes. Early during training, the network exhibited a slow and highly accurate regime, where almost all neurons fired before the network made a decision.

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Later in training, the network spontaneously shifted into a fast but slightly less accurate regime. This behaviour was intriguing, as we did not optimize for it explicitly. This is reminiscent of the trade-off between speed and accuracy in human decision-making. The figures show a raster plot of spike times of individual neurons in individual layers, with synchronization pulses shown in orange. We were also able to recover representations of the digits learned by the spiking network by gradually adjusting a blank input image to maximize the response of a target output neuron.

Having interpretable representations is important in order to understand what the network is truly learning and to prevent a small change in input from causing a large change in the result. This work is one example of an initial step that project Ihmehimmeli is taking in exploring the potential of time-based biology-inspired computing. In other on-going experiments, we are training spiking networks with temporal coding to control the walking of an artificial insect in a virtual environment, or taking inspiration from the development of the neural system to train a 2D spiking grid to predict words using axonal growth.

Our goal is to increase our familiarity with the mechanisms that nature has evolved for natural intelligence, enabling the exploration of time-based artificial neural networks with varying internal states and state transitions. We are grateful for all discussions and feedback on this work that we received from our colleagues at Google. Google at Interspeech Sunday, September 15, Over 2, experts in speech-related research fields gather to take part in oral presentations and poster sessions and to collaborate with streamed events across the globe.

As a Gold Sponsor of Interspeech , we are excited to present 30 research publications, and demonstrate some of the impact speech technology has made in our products, from accessible, automatic video captioning to a more robust, reliable Google Assistant. You can also learn more about the Google research being presented at Interspeech below Google affiliations in blue. This can lead to suboptimal referrals, delays in care, and errors in diagnosis and treatment. Existing strategies for non-dermatologists to improve diagnostic accuracy include the use of reference textbooks, online resources, and consultation with a colleague.

Machine learning tools have also been developed with the aim of helping to improve diagnostic accuracy. Previous research has largely focused on early screening of skin cancer, in particular, whether a lesion is malignant or benign , or whether a lesion is melanoma. Our results showed that a DLS can achieve an accuracy across 26 skin conditions that is on par with U.

This study highlights the potential of the DLS to augment the ability of general practitioners who did not have additional specialty training to accurately diagnose skin conditions. DLS Design Clinicians often face ambiguous cases for which there is no clear cut answer.

Rather than giving just one diagnosis, clinicians generate a differential diagnosis , which is a ranked list of possible diagnoses. A differential diagnosis frames the problem so that additional workup laboratory tests, imaging, procedures, consultations and treatments can be systematically applied until a diagnosis is confirmed.

As such, a deep learning system DLS that produces a ranked list of possible skin conditions for a skin complaint closely mimics how clinicians think and is key to prompt triage, diagnosis and treatment for patients. To render this prediction, the DLS processes inputs, including one or more clinical images of the skin abnormality and up to 45 types of metadata self-reported components of the medical history such as age, sex, symptoms, etc.

For each case, multiple images were processed using the Inception-v4 neural network architecture and combined with feature-transformed metadata, for use in the classification layer. In our study, we developed and evaluated the DLS with 17, de-identified cases that were primarily referred from primary care clinics to a teledermatology service. Data from were used for training and data from for evaluation. During model training, the DLS leveraged over 50, differential diagnoses provided by over 40 dermatologists.

Schematic of the DLS and how the reference standard ground truth was derived via the voting of three board-certified dermatologists for each case in the validation set.

Because typical differential diagnoses provided by clinicians only contain up to three diagnoses, we compared only the top three predictions by the DLS with the clinicians. This high top-3 accuracy suggests that the DLS may help prompt clinicians including dermatologists to consider possibilities that were not originally in their differential diagnoses, thus improving diagnostic accuracy and condition management. Assessing Demographic Performance Skin type, in particular, is highly relevant to dermatology, where visual assessment of the skin itself is crucial to diagnosis.

Left: An example of a case with hair loss that was challenging for non-specialists to arrive at the specific diagnosis, which is necessary for determining appropriate treatment. Right: An image with regions highlighted in green showing the areas that the DLS identified as important and used to make its prediction. Center: The combined image, which indicates that the DLS mostly focused on the area with hair loss to make this prediction, instead of on forehead skin color, for example, which may indicate potential bias.

Much like how having images from several angles can help a teledermatologist more accurately diagnose a skin condition, the accuracy of the DLS improves with increasing number of images. If metadata e. This accuracy gap, which may occur in scenarios where no medical history is available, can be partially mitigated by training the DLS with only images. Nevertheless, this data suggests that providing the answers to a few questions about the skin condition can substantially improve the DLS accuracy.

The DLS performance improves when more images blue line or metadata blue compared with red line are present. In the absence of metadata as input, training a separate DLS using images alone leads to a marginal improvement compared to the current DLS green line. Future Work and Applications Though these results are very promising, much work remains ahead. First, as reflective of real-world practice, the relative rarity of skin cancer such as melanoma in our dataset hindered our ability to train an accurate system to detect cancer.

Related to this, the skin cancer labels in our dataset were not biopsy-proven, limiting the quality of the ground truth in this regard. Second, while our dataset did contain a variety of Fitzpatrick skin types, some skin types were too rare in this dataset to allow meaningful training or analysis. Finally, the validation dataset was from one teledermatology service.

Though 17 primary care locations across two states were included, additional validation on cases from a wider geographical region will be critical. We believe these limitations can be addressed by including more cases of biopsy-proven skin cancers in the training and validation sets, and including cases representative of additional Fitzpatrick skin types and from other clinical centers. For example, such a DLS could help triage cases to guide prioritization for clinical care or could help non-dermatologists initiate dermatologic care more accurately and potentially improve access.

Though significant work remains, we are excited for future efforts in examining the usefulness of such a system for clinicians. For research collaboration inquiries, please contact dermatology-research google. Acknowledgements This work involved the efforts of a multidisciplinary team of software engineers, researchers, clinicians and cross functional contributors. Corrado, Lily H. Peng, Dale R. Carter Dunn and David Coz. Last but not least, this work would not have been possible without the participation of the dermatologists, primary care physicians, nurse practitioners who reviewed cases for this study, Sabina Bis who helped to establish the skin condition mapping and Amy Paller who provided feedback on the manuscript.

Posted by Chen Sun and Cordelia Schmid, Research Scientists, Google Research While people can easily recognize what activities are taking place in videos and anticipate what events may happen next, it is much more difficult for machines. Yet, increasingly, it is important for machines to understand the contents and dynamics of videos for applications, such as temporal localization , action detection and navigation for self-driving cars.

In order to train neural networks to perform such tasks, it is common to use supervised training, in which the training data consists of videos that have been meticulously labeled by people on a frame-by-frame basis. Such annotations are hard to acquire at scale. Consequently, there is much interest in self-supervised learning, in which models are trained on various proxy tasks, and the supervision of those tasks naturally resides in the data itself.

The goal is to discover high-level semantic features that correspond to actions and events that unfold over longer time scales. To accomplish this, we exploit the key insight that human language has evolved words to describe high-level objects and events. In videos, speech tends to be temporally aligned with the visual signals, and can be extracted by using off-the-shelf automatic speech recognition ASR systems, and thus provides a natural source of self-supervision.

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Our model is an example of cross-modal learning , as it jointly utilizes the signals from visual and audio speech modalities during training. Image frames and human speech from the same video locations are often semantically aligned. The alignment is non-exhaustive and sometimes noisy, which we hope to mitigate by pretraining on larger datasets.

A BERT Model for Videos The first step of representation learning is to define a proxy task that leads the model to learn temporal dynamics and cross-modal semantic correspondence from long, unlabeled videos. The BERT model has shown state-of-the-art performance on various natural language processing tasks, by applying the Transformer architecture to encode long sequences, and pretraining on a corpus containing a large amount of text.

BERT uses the cloze test as its proxy task, in which the BERT model is forced to predict missing words from context bidirectionally, instead of just predicting the next word in a sequence. The image frames are converted into visual tokens with durations of 1. They are then concatenated with the ASR word tokens. Our hypothesis, which our experiments support, is that by pretraining on this proxy task, the model learns to reason about longer-range temporal dynamics visual cloze and high-level semantics visual-text cloze.

Illustration of VideoBERT in the context of a video and text masked token prediction, or cloze, task. Some visual and text tokens are masked out. Yellow and pink boxes correspond to the input and output embeddings, respectively. Top : the training objective is to recover the correct tokens for the masked locations. Once trained, one can inspect what the VideoBERT model learns on a number of tasks to verify that the output accurately reflects the video content.

For example, text-to-video prediction can be used to automatically generate a set of instructions such as a recipe from video, yielding video segments tokens that reflect what is described at each step. In addition, video-to-video prediction can be used to visualize possible future content based on an initial video token. Top: Given some recipe text, we generate a sequence of visual tokens.

In this case, the model predicts that a bowl of flour and cocoa powder may be baked in an oven, and may become a brownie or cupcake. We visualize the visual tokens using the images from the training set closest to the tokens in feature space. Transfer Learning with Contrastive Bidirectional Transformers While VideoBERT showed impressive results in learning how to automatically label and predict video content, we noticed that the visual tokens used by VideoBERT can lose fine-grained visual information, such as smaller objects and subtle motions.

To explore this, we propose the Contrastive Bidirectional Transformers CBT model which removes this tokenization step, and further evaluated the quality of learned representations by transfer learning on downstream tasks. CBT applies a different loss function, the contrastive loss , in order to maximize the mutual information between the masked positions and the rest of cross-modal sentences. We evaluated the learned representations for a diverse set of tasks e. The CBT approach outperforms previous state-of-the-art by significant margins on most benchmarks. We observe that: 1 the cross-modal objective is important for transfer learning performance; 2 a bigger and more diverse pre-training set leads to better representations; 3 compared with baseline methods such as average pooling or LSTMs , the CBT model is much better at utilizing long temporal context.

Action anticipation accuracy with the CBT approach from untrimmed videos with activity classes. We find that our models are not only useful for zero-shot action classification and recipe generation, but the learned temporal representations also transfer well to various downstream tasks, such as action anticipation. Future work includes learning low-level visual features jointly with long-term temporal representations, which enables better adaptation to the video context.

Furthermore, we plan to expand the number of pre-training videos to be larger and more diverse. Posted by Badih Ghazi and Joshua R. Wang, Research Scientists, Google Research Much of classical machine learning ML focuses on utilizing available data to make more accurate predictions. More recently, researchers have considered other important objectives, such as how to design algorithms to be small , efficient , and robust. Sketching is a rich field of study that dates back to the foundational work of Alon, Matias, and Szegedy , which can enable neural networks to efficiently summarize information about their inputs.

For example: Imagine stepping into a room and briefly viewing the objects within. How big is said cat?

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Was it usually morning or night when we saw the room? However, can one design systems that are also capable of efficiently answering such memory-based questions even if they are unknown at training time? Basic Sketching Algorithms In general, sketching algorithms take a vector x and produce an output sketch vector that behaves like x but whose storage cost is much smaller. The fact that the storage cost is much smaller allows one to succinctly store information about the network, which is critical for efficiently answering memory-based questions.

In the simplest case, a linear sketch x is given by the matrix-vector product Ax where A is a wide matrix, i. Such methods have led to a variety of efficient algorithms for basic tasks on massive datasets, such as estimating fundamental statistics e.

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This basic approach works well in the relatively simple case of linear regression , where it is possible to identify important data dimensions simply by the magnitude of weights under the common assumption that they have uniform variance. However, many modern machine learning models are actually deep neural networks and are based on high-dimensional embeddings such as Word2Vec , Image Embeddings , Glove , DeepWalk and BERT , which makes the task of summarizing the operation of the model on the input much more difficult.

However, a large subset of these more complex networks are modular , allowing us to generate accurate sketches of their behavior, in spite of their complexity. It is also possible to split other canonical architectures to view them as modular networks and apply our approach. For example, convolutional neural networks CNNs are traditionally understood to behave in a modular fashion; they detect basic concepts and attributes in their lower layers and build up to detecting more complex objects in their higher layers.

In this view, the convolution kernels correspond to modules. A cartoon depiction of a modular network is given below. This is a cartoon depiction of a modular network for image processing. Data flows from the bottom of the figure to the top through the modules represented with blue boxes. Note that modules in the lower layers correspond to basic objects, such as edges in an image, while modules in upper layers correspond to more complex objects, like humans or cats.

Also notice that in this imaginary modular network, the output of the face module is generic enough to be used by both the human and cat modules. Sketch Requirements To optimize our approach for these modular networks, we identified several desired properties that a network sketch should satisfy: Sketch-to-Sketch Similarity: The sketches of two unrelated network operations either in terms of the present modules or in terms of the attribute vectors should be very different; on the other hand, the sketches of two similar network operations should be very close.

Attribute Recovery: The attribute vector, e. Summary Statistics: If there are multiple similar objects, we can recover summary statistics about them. For example, if an image has multiple cats, we can count how many there are. Note that we want to do this without knowing the questions ahead of time. Graceful Erasure : Erasing a suffix of the top-level sketch maintains the above properties but would smoothly increase the error. Network Recovery: Given sufficiently many input, sketch pairs, the wiring of the edges of the network as well as the sketch function can be approximately recovered.

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This is a 2D cartoon depiction of the sketch-to-sketch similarity property. Each vector represents a sketch and related sketches are more likely to cluster together. By companies having a full understanding of all resources available and a highly adaptable robots the goal is to eventually make manufactures providing mass customization possible. How it would work is that a company would decide they want to produce specific limit run object, like a special coffee table.

The company would submit their design and the system would automatically start a bidding process among facilities that have the equipment and time to handle the order. It would allow suppliers to automatically derive production plans and offer them in real time to potential buyers. The goal is a rapid turn around from design to delivery. General Electric is the 31st largest company in the world by revenue and one of the largest and most diverse manufacturers on the planet, making everything from large industrial equipment to home appliances.

It has over factories around the world and has only begun transforming them into smart facilities. In GE launched its Brilliant Manufacturing Suite for customers, which it had been field testing in its own factories. The system takes a holistic approach of tracking and processing everything in the manufacturing process to find possible issues before they emerge and to detect inefficiencies. GE claims it improved equipment effectiveness at this facility by 18 percent. It is powered by Predix, their industrial internet of things platform. In the manufacturing space, Predix can use sensors to automatically capture every step of the process and monitor each piece of complex equipment.

With that data, the Predix deep learning capabilities can spot potential problems and possible solutions. GE now has seven Brilliant Factories, powered by their Predix system, that serve as test cases. It claims positive improvements at each. For example, according to GE their system result in, their wind generator factory in Vietnam increasing productivity by 5 percent and its jet engine factory in Muskegon had a 25 percent better on-time delivery rate.

They claim it has also cut unplanned downtime by percent by equipping machines with smart sensors to detect wear. While GE and Siemens are heavily focused on applying AI to create a holistic manufacturing process, other companies that specialize in industrial robotics are focusing on making robots smarter. Fanuc, the Japanese company which is a leader in industrial robotics, has recently made a strong push for greater connectivity and AI usage within their equipment. It is described as an industrial internet of things platform for manufacturing.

Fanuc is using deep reinforcement learning to help some of its industrial robots train themselves. They perform the same task over and over again, learning each time until they achieve sufficient accuracy. The idea is that what could take one robot eight hours to learn, eight robots can learn in one hour. Fast learning means less downtime and the ability to handle more varied products at the same factory. While humans had to initially program every specific action an industrial robot takes, we eventually developed robots that could learn for themselves.

In the future, more and more robots may be able to transfer their skills and and learn together. Robot application with relatively repetitive tasks fast food robots being a good candidate are the low-hanging fruit for this kind of transfer learning. The video below, shows how a FUNAC robot autonomously learns to pick up iron cylinders positioned at random angles:. KUKA, the Chinese-owned German manufacturing company, is one of the world largest manufacturers of industrial robots in the world.

One use of AI they have been investing in is helping to improve human-robot collaboration. Most industrial robots were very strong and stupid, which meant getting near them while they worked was a major health hazard requiring safety barriers between people and machines. The video shows how the robots are being used at a BMW factory. They can also quickly be reassigned to new tasks basically anywhere in the factory as needs change. They hold the potential to improve efficiency and flexibility in factories.

Automation, robotics, and complex analytics have all been used by the manufacturing industry for years. For decades entire businesses and academic fields have existed for looking at data in manufacturing to find ways reduce waste and improve efficiency. Manufacturing is already a reasonably streamlined and technically advanced field. As a result — unlike some industries such as taxi services where the deployment of more advanced AI is likely to cause massive disruption — the near term use of new AI technology in the manufacturing industry is more likely to look like evolution than a revolution.

Greater industrial connectivity, more widely deployed sensors, more powerful analytics, and improved robots are all able to squeeze out noticeable but modest improvements in efficiency or flexibility. We are seeing these newer applications of machine learning produce relatively modest reductions in equipment failures, better on-time deliveries, slight improvements in equipment, and faster training times in the competitive world of industrial robotics.

These the improvements may seem small but when added together and spread over such a large sector the total potential saves is significant. This is why companies are spending billions on developing AI tools to squeeze a few extra percentage points out of different factories. Long-term, the total digital integration and the advanced automation of the entire design and production process could open up some interesting possibilities.

Customization is rare and expensive while high-volume, mass produced goods are the dominant model in manufacturing, since currently the cost of redesigning a factory line for new products is often excessive. Consumers for the most part have been willing to make the trade off because mass produced goods are so much cheaper.

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