One of the seminal texts for anyone interested in technology and society is Melvin Kranzbergs Six Laws of Technology, the first of which says that technology is neither good nor bad; nor is it neutral. By this, Kranzberg meant that technologys interaction with society is such that technical developments frequently have environmental, social and human consequences that go far beyond the immediate purposes of the technical devices and practices themselves, and the same technology can have quite different results when introduced into different contexts or under different circumstances.

The saloon-bar version of this is that technology is both good and bad; it all depends on how its used a tactic that tech evangelists regularly deploy as a way of stopping the conversation. So a better way of using Kranzbergs law is to ask a simple Latin question: Cui bono? who benefits from any proposed or hyped technology? And, by implication, who loses?

With any general-purpose technology which is what the internet has become the answer is going to be complicated: various groups, societies, sectors, maybe even continents win and lose, so in the end the question comes down to: who benefits most? For the internet as a whole, its too early to say. But when we focus on a particular digital technology, then things become a bit clearer.

A case in point is the technology known as machine learning, a manifestation of artificial intelligence that is the tech obsession de nos jours. Its really a combination of algorithms that are trained on big data, ie huge datasets. In principle, anyone with the computational skills to use freely available software tools such as TensorFlow could do machine learning. But in practice they cant because they dont have access to the massive data needed to train their algorithms.

This means the outfits where most of the leading machine-learning research is being done are a small number of tech giants especially Google, Facebook and Amazon which have accumulated colossal silos of behavioural data over the last two decades. Since they have come to dominate the technology, the Kranzberg question who benefits? is easy to answer: they do. Machine learning now drives everything in those businesses personalisation of services, recommendations, precisely targeted advertising, behavioural prediction For them, AI (by which they mostly mean machine learning) is everywhere. And it is making them the most profitable enterprises in the history of capitalism.

As a consequence, a powerful technology with great potential for good is at the moment deployed mainly for privatised gain. In the process, it has been characterised by unregulated premature deployment, algorithmic bias, reinforcing inequality, undermining democratic processes and boosting covert surveillance to toxic levels. That it doesnt have to be like this was vividly demonstrated last week with a report in the leading biological journal Cell of an extraordinary project, which harnessed machine learning in the public (as compared to the private) interest. The researchers used the technology to tackle the problem of bacterial resistance to conventional antibiotics a problem that is rising dramatically worldwide, with predictions that, without a solution, resistant infections could kill 10 million people a year by 2050.

The team of MIT and Harvard researchers built a neural network (an algorithm inspired by the brains architecture) and trained it to spot molecules that inhibit the growth of the Escherichia coli bacterium using a dataset of 2,335 molecules for which the antibacterial activity was known including a library of 300 existing approved antibiotics and 800 natural products from plant, animal and microbial sources. They then asked the network to predict which would be effective against E coli but looked different from conventional antibiotics. This produced a hundred candidates for physical testing and led to one (which they named halicin after the HAL 9000 computer from 2001: A Space Odyssey) that was active against a wide spectrum of pathogens notably including two that are totally resistant to current antibiotics and are therefore a looming nightmare for hospitals worldwide.

There are a number of other examples of machine learning for public good rather than private gain. One thinks, for example, of the collaboration between Google DeepMind and Moorfields eye hospital. But this new example is the most spectacular to date because it goes beyond augmenting human screening capabilities to aiding the process of discovery. So while the main beneficiaries of machine learning for, say, a toxic technology like facial recognition are mostly authoritarian political regimes and a range of untrustworthy or unsavoury private companies, the beneficiaries of the technology as an aid to scientific discovery could be humanity as a species. The technology, in other words, is both good and bad. Kranzbergs first law rules OK.

Every cloud Zeynep Tufekci has written a perceptive essay for the Atlantic about how the coronavirus revealed authoritarianisms fatal flaw.

EU ideas explained Politico writers Laura Kayali, Melissa Heikkil and Janosch Delcker have delivered a shrewd analysis of the underlying strategy behind recent policy documents from the EU dealing with the digital future.

On the nature of loss Jill Lepore has written a knockout piece for the New Yorker under the heading The lingering of loss, on friendship, grief and remembrance. One of the best things Ive read in years.

See the rest here:
How artificial intelligence outsmarted the superbugs - The Guardian

30MHz is participating in the autonomous greenhouse challenge: growing tomatoes without entering the greenhouse. Theyre managing the greenhouse from behind their laptops, and have to guide their decisions based on the real-time data they receive from the indoor climate, outside conditions and weather forecasts. To be able to do this theyve been developing multiple machine learning applications. These applications guide the cultivation strategy and subsequently, the actions taken to reach the desired climate.

Machine learning challengesHowever, there are many challenges in developing and operationalising large scale machine learning applications. One reason is the inherent nature of machine learning. Data are ever-evolving and models are stochastic, which means you have no certainty about what will happen in advance.

In software engineering, code is version controlled to manage changes over time (i.e. the numbered software updates of your smartphone). In machine learning, there are no standardised solutions to manage changes in code, data and model characteristics at the same time. And this is largely due to the (im)maturity of the field. There are many initiatives trying to solve this problem, for example, MLflow and Data Version Control (DVC), but these have their own limitations which are out the scope of this blog.

AWS project & solutionsTo solve some of these problems 30MHz has been fortunate to receive the help of two machine learning engineers from Amazon Web Services (or AWS). AWS is a cloud provider, and the company is using their services to host among others servers, database and machine learning models. As a company, 30MHz has been closely working together with AWS for quite some years. For this reason, and because theyre excited about the work, 30MHz had the opportunity to learn from and work with AWS engineers at their own office in Amsterdam for more than two weeks.

The goals of the project were twofold:

Improve and automateWith AWS' knowledge and experience, 30MHz has been able to improve and automate a large part of their machine learning infrastructure. The result is a scalable and robust framework for machine learning applications on the 30MHz platform.

For more information:30MHzMoezelhavenweg 91043AM AmsterdamNetherlands+31 (0) 6 14551362contact@30mhz.comwww.30mhz.com

Go here to see the original:
Growing tomatoes with Amazon Web Services - hortidaily.com

Machine learning is enabling computers to tackle tasks that have, until now, only been carried out by people.

From driving cars to translating speech, machine learning is driving an explosion in the capabilities of artificial intelligence -- helping software make sense of the messy and unpredictable real world.

But what exactly is machine learning and what is making the current boom in machine learning possible?

At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data.

Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a self-driving car, whether the use of the word book in a sentence relates to a paperback or a hotel reservation, whether an email is spam, or recognizing speech accurately enough to generate captions for a YouTube video.

The key difference from traditional computer software is that a human developer hasn't written code that instructs the system how to tell the difference between the banana and the apple.

Instead a machine-learning model has been taught how to reliably discriminate between the fruits by being trained on a large amount of data, in this instance likely a huge number of images labelled as containing a banana or an apple.

Data, and lots of it, is the key to making machine learning possible.

Machine learning may have enjoyed enormous success of late, but it is just one method for achieving artificial intelligence.

At the birth of the field of AI in the 1950s, AI was defined as any machine capable of performing a task that would typically require human intelligence.

AI systems will generally demonstrate at least some of the following traits: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity.

Alongside machine learning, there are various other approaches used to build AI systems, including evolutionary computation, where algorithms undergo random mutations and combinations between generations in an attempt to "evolve" optimal solutions, and expert systems, where computers are programmed with rules that allow them to mimic the behavior of a human expert in a specific domain, for example an autopilot system flying a plane.

Machine learning is generally split into two main categories: supervised and unsupervised learning.

This approach basically teaches machines by example.

During training for supervised learning, systems are exposed to large amounts of labelled data, for example images of handwritten figures annotated to indicate which number they correspond to. Given sufficient examples, a supervised-learning system would learn to recognize the clusters of pixels and shapes associated with each number and eventually be able to recognize handwritten numbers, able to reliably distinguish between the numbers 9 and 4 or 6 and 8.

However, training these systems typically requires huge amounts of labelled data, with some systems needing to be exposed to millions of examples to master a task.

As a result, the datasets used to train these systems can be vast, with Google's Open Images Dataset having about nine million images, its labeled video repository YouTube-8M linking to seven million labeled videos and ImageNet, one of the early databases of this kind, having more than 14 million categorized images. The size of training datasets continues to grow, with Facebook recently announcing it had compiled 3.5 billion images publicly available on Instagram, using hashtags attached to each image as labels. Using one billion of these photos to train an image-recognition system yielded record levels of accuracy -- of 85.4 percent -- on ImageNet's benchmark.

The laborious process of labeling the datasets used in training is often carried out using crowdworking services, such as Amazon Mechanical Turk, which provides access to a large pool of low-cost labor spread across the globe. For instance, ImageNet was put together over two years by nearly 50,000 people, mainly recruited through Amazon Mechanical Turk. However, Facebook's approach of using publicly available data to train systems could provide an alternative way of training systems using billion-strong datasets without the overhead of manual labeling.

In contrast, unsupervised learning tasks algorithms with identifying patterns in data, trying to spot similarities that split that data into categories.

An example might be Airbnb clustering together houses available to rent by neighborhood, or Google News grouping together stories on similar topics each day.

The algorithm isn't designed to single out specific types of data, it simply looks for data that can be grouped by its similarities, or for anomalies that stand out.

The importance of huge sets of labelled data for training machine-learning systems may diminish over time, due to the rise of semi-supervised learning.

As the name suggests, the approach mixes supervised and unsupervised learning. The technique relies upon using a small amount of labelled data and a large amount of unlabelled data to train systems. The labelled data is used to partially train a machine-learning model, and then that partially trained model is used to label the unlabelled data, a process called pseudo-labelling. The model is then trained on the resulting mix of the labelled and pseudo-labelled data.

The viability of semi-supervised learning has been boosted recently by Generative Adversarial Networks ( GANs), machine-learning systems that can use labelled data to generate completely new data, for example creating new images of Pokemon from existing images, which in turn can be used to help train a machine-learning model.

Were semi-supervised learning to become as effective as supervised learning, then access to huge amounts of computing power may end up being more important for successfully training machine-learning systems than access to large, labelled datasets.

A way to understand reinforcement learning is to think about how someone might learn to play an old school computer game for the first time, when they aren't familiar with the rules or how to control the game. While they may be a complete novice, eventually, by looking at the relationship between the buttons they press, what happens on screen and their in-game score, their performance will get better and better.

An example of reinforcement learning is Google DeepMind's Deep Q-network, which has beaten humans in a wide range of vintage video games. The system is fed pixels from each game and determines various information about the state of the game, such as the distance between objects on screen. It then considers how the state of the game and the actions it performs in game relate to the score it achieves.

Over the process of many cycles of playing the game, eventually the system builds a model of which actions will maximize the score in which circumstance, for instance, in the case of the video game Breakout, where the paddle should be moved to in order to intercept the ball.

Everything begins with training a machine-learning model, a mathematical function capable of repeatedly modifying how it operates until it can make accurate predictions when given fresh data.

Before training begins, you first have to choose which data to gather and decide which features of the data are important.

A hugely simplified example of what data features are is given in this explainer by Google, where a machine learning model is trained to recognize the difference between beer and wine, based on two features, the drinks' color and their alcoholic volume (ABV).

Each drink is labelled as a beer or a wine, and then the relevant data is collected, using a spectrometer to measure their color and hydrometer to measure their alcohol content.

An important point to note is that the data has to be balanced, in this instance to have a roughly equal number of examples of beer and wine.

The gathered data is then split, into a larger proportion for training, say about 70 percent, and a smaller proportion for evaluation, say the remaining 30 percent. This evaluation data allows the trained model to be tested to see how well it is likely to perform on real-world data.

Before training gets underway there will generally also be a data-preparation step, during which processes such as deduplication, normalization and error correction will be carried out.

The next step will be choosing an appropriate machine-learning model from the wide variety available. Each have strengths and weaknesses depending on the type of data, for example some are suited to handling images, some to text, and some to purely numerical data.

Basically, the training process involves the machine-learning model automatically tweaking how it functions until it can make accurate predictions from data, in the Google example, correctly labeling a drink as beer or wine when the model is given a drink's color and ABV.

A good way to explain the training process is to consider an example using a simple machine-learning model, known as linear regression with gradient descent. In the following example, the model is used to estimate how many ice creams will be sold based on the outside temperature.

Imagine taking past data showing ice cream sales and outside temperature, and plotting that data against each other on a scatter graph -- basically creating a scattering of discrete points.

To predict how many ice creams will be sold in future based on the outdoor temperature, you can draw a line that passes through the middle of all these points, similar to the illustration below.

Once this is done, ice cream sales can be predicted at any temperature by finding the point at which the line passes through a particular temperature and reading off the corresponding sales at that point.

Bringing it back to training a machine-learning model, in this instance training a linear regression model would involve adjusting the vertical position and slope of the line until it lies in the middle of all of the points on the scatter graph.

At each step of the training process, the vertical distance of each of these points from the line is measured. If a change in slope or position of the line results in the distance to these points increasing, then the slope or position of the line is changed in the opposite direction, and a new measurement is taken.

In this way, via many tiny adjustments to the slope and the position of the line, the line will keep moving until it eventually settles in a position which is a good fit for the distribution of all these points, as seen in the video below. Once this training process is complete, the line can be used to make accurate predictions for how temperature will affect ice cream sales, and the machine-learning model can be said to have been trained.

While training for more complex machine-learning models such as neural networks differs in several respects, it is similar in that it also uses a "gradient descent" approach, where the value of "weights" that modify input data are repeatedly tweaked until the output values produced by the model are as close as possible to what is desired.

Once training of the model is complete, the model is evaluated using the remaining data that wasn't used during training, helping to gauge its real-world performance.

To further improve performance, training parameters can be tuned. An example might be altering the extent to which the "weights" are altered at each step in the training process.

A very important group of algorithms for both supervised and unsupervised machine learning are neural networks. These underlie much of machine learning, and while simple models like linear regression used can be used to make predictions based on a small number of data features, as in the Google example with beer and wine, neural networks are useful when dealing with large sets of data with many features.

Neural networks, whose structure is loosely inspired by that of the brain, are interconnected layers of algorithms, called neurons, which feed data into each other, with the output of the preceding layer being the input of the subsequent layer.

Each layer can be thought of as recognizing different features of the overall data. For instance, consider the example of using machine learning to recognize handwritten numbers between 0 and 9. The first layer in the neural network might measure the color of the individual pixels in the image, the second layer could spot shapes, such as lines and curves, the next layer might look for larger components of the written number -- for example, the rounded loop at the base of the number 6. This carries on all the way through to the final layer, which will output the probability that a given handwritten figure is a number between 0 and 9.

See more: Special report: How to implement AI and machine learning (free PDF)

The network learns how to recognize each component of the numbers during the training process, by gradually tweaking the importance of data as it flows between the layers of the network. This is possible due to each link between layers having an attached weight, whose value can be increased or decreased to alter that link's significance. At the end of each training cycle the system will examine whether the neural network's final output is getting closer or further away from what is desired -- for instance is the network getting better or worse at identifying a handwritten number 6. To close the gap between between the actual output and desired output, the system will then work backwards through the neural network, altering the weights attached to all of these links between layers, as well as an associated value called bias. This process is called back-propagation.

Eventually this process will settle on values for these weights and biases that will allow the network to reliably perform a given task, such as recognizing handwritten numbers, and the network can be said to have "learned" how to carry out a specific task

An illustration of the structure of a neural network and how training works.

A subset of machine learning is deep learning, where neural networks are expanded into sprawling networks with a huge number of layers that are trained using massive amounts of data. It is these deep neural networks that have fueled the current leap forward in the ability of computers to carry out task like speech recognition and computer vision.

There are various types of neural networks, with different strengths and weaknesses. Recurrent neural networks are a type of neural net particularly well suited to language processing and speech recognition, while convolutional neural networks are more commonly used in image recognition. The design of neural networks is also evolving, with researchers recently devising a more efficient design for an effective type of deep neural network called long short-term memory or LSTM, allowing it to operate fast enough to be used in on-demand systems like Google Translate.

The AI technique of evolutionary algorithms is even being used to optimize neural networks, thanks to a process called neuroevolution. The approach was recently showcased by Uber AI Labs, which released papers on using genetic algorithms to train deep neural networks for reinforcement learning problems.

While machine learning is not a new technique, interest in the field has exploded in recent years.

This resurgence comes on the back of a series of breakthroughs, with deep learning setting new records for accuracy in areas such as speech and language recognition, and computer vision.

What's made these successes possible are primarily two factors, one being the vast quantities of images, speech, video and text that is accessible to researchers looking to train machine-learning systems.

But even more important is the availability of vast amounts of parallel-processing power, courtesy of modern graphics processing units (GPUs), which can be linked together into clusters to form machine-learning powerhouses.

Today anyone with an internet connection can use these clusters to train machine-learning models, via cloud services provided by firms like Amazon, Google and Microsoft.

As the use of machine-learning has taken off, so companies are now creating specialized hardware tailored to running and training machine-learning models. An example of one of these custom chips is Google's Tensor Processing Unit (TPU), the latest version of which accelerates the rate at which machine-learning models built using Google's TensorFlow software library can infer information from data, as well as the rate at which they can be trained.

These chips are not just used to train models for Google DeepMind and Google Brain, but also the models that underpin Google Translate and the image recognition in Google Photo, as well as services that allow the public to build machine learning models using Google's TensorFlow Research Cloud. The second generation of these chips was unveiled at Google's I/O conference in May last year, with an array of these new TPUs able to train a Google machine-learning model used for translation in half the time it would take an array of the top-end GPUs, and the recently announced third-generation TPUs able to accelerate training and inference even further.

As hardware becomes increasingly specialized and machine-learning software frameworks are refined, it's becoming increasingly common for ML tasks to be carried out on consumer-grade phones and computers, rather than in cloud datacenters. In the summer of 2018, Google took a step towards offering the same quality of automated translation on phones that are offline as is available online, by rolling out local neural machine translation for 59 languages to the Google Translate app for iOS and Android.

Perhaps the most famous demonstration of the efficacy of machine-learning systems was the 2016 triumph of the Google DeepMind AlphaGo AI over a human grandmaster in Go, a feat that wasn't expected until 2026. Go is an ancient Chinese game whose complexity bamboozled computers for decades. Go has about 200 moves per turn, compared to about 20 in Chess. Over the course of a game of Go, there are so many possible moves that searching through each of them in advance to identify the best play is too costly from a computational standpoint. Instead, AlphaGo was trained how to play the game by taking moves played by human experts in 30 million Go games and feeding them into deep-learning neural networks.

Training the deep-learning networks needed can take a very long time, requiring vast amounts of data to be ingested and iterated over as the system gradually refines its model in order to achieve the best outcome.

However, more recently Google refined the training process with AlphaGo Zero, a system that played "completely random" games against itself, and then learnt from the results. At last year's prestigious Neural Information Processing Systems (NIPS) conference, Google DeepMind CEO Demis Hassabis revealed AlphaGo had also mastered the games of chess and shogi.

DeepMind continue to break new ground in the field of machine learning. In July 2018, DeepMind reported that its AI agents had taught themselves how to play the 1999 multiplayer 3D first-person shooter Quake III Arena, well enough to beat teams of human players. These agents learned how to play the game using no more information than the human players, with their only input being the pixels on the screen as they tried out random actions in game, and feedback on their performance during each game.

More recently DeepMind demonstrated an AI agent capable of superhuman performance across multiple classic Atari games, an improvement over earlier approaches where each AI agent could only perform well at a single game. DeepMind researchers say these general capabilities will be important if AI research is to tackle more complex real-world domains.

Machine learning systems are used all around us, and are a cornerstone of the modern internet.

Machine-learning systems are used to recommend which product you might want to buy next on Amazon or video you want to may want to watch on Netflix.

Every Google search uses multiple machine-learning systems, to understand the language in your query through to personalizing your results, so fishing enthusiasts searching for "bass" aren't inundated with results about guitars. Similarly Gmail's spam and phishing-recognition systems use machine-learning trained models to keep your inbox clear of rogue messages.

One of the most obvious demonstrations of the power of machine learning are virtual assistants, such as Apple's Siri, Amazon's Alexa, the Google Assistant, and Microsoft Cortana.

Each relies heavily on machine learning to support their voice recognition and ability to understand natural language, as well as needing an immense corpus to draw upon to answer queries.

But beyond these very visible manifestations of machine learning, systems are starting to find a use in just about every industry. These exploitations include: computer vision for driverless cars, drones and delivery robots; speech and language recognition and synthesis for chatbots and service robots; facial recognition for surveillance in countries like China; helping radiologists to pick out tumors in x-rays, aiding researchers in spotting genetic sequences related to diseases and identifying molecules that could lead to more effective drugs in healthcare; allowing for predictive maintenance on infrastructure by analyzing IoT sensor data; underpinning the computer vision that makes the cashierless Amazon Go supermarket possible, offering reasonably accurate transcription and translation of speech for business meetings -- the list goes on and on.

Deep-learning could eventually pave the way for robots that can learn directly from humans, with researchers from Nvidia recently creating a deep-learning system designed to teach a robot to how to carry out a task, simply by observing that job being performed by a human.

As you'd expect, the choice and breadth of data used to train systems will influence the tasks they are suited to.

For example, in 2016 Rachael Tatman, a National Science Foundation Graduate Research Fellow in the Linguistics Department at the University of Washington, found that Google's speech-recognition system performed better for male voices than female ones when auto-captioning a sample of YouTube videos, a result she ascribed to 'unbalanced training sets' with a preponderance of male speakers.

As machine-learning systems move into new areas, such as aiding medical diagnosis, the possibility of systems being skewed towards offering a better service or fairer treatment to particular groups of people will likely become more of a concern.

A heavily recommended course for beginners to teach themselves the fundamentals of machine learning is this free Stanford University and Coursera lecture series by AI expert and Google Brain founder Andrew Ng.

Another highly-rated free online course, praised for both the breadth of its coverage and the quality of its teaching, is this EdX and Columbia University introduction to machine learning, although students do mention it requires a solid knowledge of math up to university level.

Technologies designed to allow developers to teach themselves about machine learning are increasingly common, from AWS' deep-learning enabled camera DeepLens to Google's Raspberry Pi-powered AIY kits.

All of the major cloud platforms -- Amazon Web Services, Microsoft Azure and Google Cloud Platform -- provide access to the hardware needed to train and run machine-learning models, with Google letting Cloud Platform users test out its Tensor Processing Units -- custom chips whose design is optimized for training and running machine-learning models.

This cloud-based infrastructure includes the data stores needed to hold the vast amounts of training data, services to prepare that data for analysis, and visualization tools to display the results clearly.

Newer services even streamline the creation of custom machine-learning models, with Google recently revealing a service that automates the creation of AI models, called Cloud AutoML. This drag-and-drop service builds custom image-recognition models and requires the user to have no machine-learning expertise, similar to Microsoft's Azure Machine Learning Studio. In a similar vein, Amazon recently unveiled new AWS offerings designed to accelerate the process of training up machine-learning models.

For data scientists, Google's Cloud ML Engine is a managed machine-learning service that allows users to train, deploy and export custom machine-learning models based either on Google's open-sourced TensorFlow ML framework or the open neural network framework Keras, and which now can be used with the Python library sci-kit learn and XGBoost.

Database admins without a background in data science can use Google's BigQueryML, a beta service that allows admins to call trained machine-learning models using SQL commands, allowing predictions to be made in database, which is simpler than exporting data to a separate machine learning and analytics environment.

For firms that don't want to build their own machine-learning models, the cloud platforms also offer AI-powered, on-demand services -- such as voice, vision, and language recognition. Microsoft Azure stands out for the breadth of on-demand services on offer, closely followed by Google Cloud Platform and then AWS.

Meanwhile IBM, alongside its more general on-demand offerings, is also attempting to sell sector-specific AI services aimed at everything from healthcare to retail, grouping these offerings together under its IBM Watson umbrella.

Early in 2018, Google expanded its machine-learning driven services to the world of advertising, releasing a suite of tools for making more effective ads, both digital and physical.

While Apple doesn't enjoy the same reputation for cutting edge speech recognition, natural language processing and computer vision as Google and Amazon, it is investing in improving its AI services, recently putting Google's former chief in charge of machine learning and AI strategy across the company, including the development of its assistant Siri and its on-demand machine learning service Core ML.

In September 2018, NVIDIA launched a combined hardware and software platform designed to be installed in datacenters that can accelerate the rate at which trained machine-learning models can carry out voice, video and image recognition, as well as other ML-related services.

The NVIDIA TensorRT Hyperscale Inference Platform uses NVIDIA Tesla T4 GPUs, which delivers up to 40x the performance of CPUs when using machine-learning models to make inferences from data, and the TensorRT software platform, which is designed to optimize the performance of trained neural networks.

There are a wide variety of software frameworks for getting started with training and running machine-learning models, typically for the programming languages Python, R, C++, Java and MATLAB.

Famous examples include Google's TensorFlow, the open-source library Keras, the Python library Scikit-learn, the deep-learning framework CAFFE and the machine-learning library Torch.

Read more from the original source:
What is machine learning? Everything you need to know | ZDNet

Dr. Luis Perez-Breva

Luis Perez-Breva is an MIT professor and the faculty director of innovation teams at the MIT School or Engineering. He is also an entrepreneur and part of The Martin Trust Center for MIT Entrepreneurship. Luis works to see how we can use technology to make our lives better and also on how we can work to get new technology out into the world. On a recent AI Today podcast, Professor Perez-Breva managed to get us to think deeply into our understanding of both artificial intelligence and machine learning.

Are we too focused on data?

Anyone who has been following artificial intelligence and machine learning knows the vital centrality of data. Without data, we cant train machine learning models. And without machine learning models, we dont have a way for systems to learn from experience. Surely, data needs to be the center of our attention to make AI systems a reality.

However, Dr. Perez-Breva thinks that we are overly focusing on data and perhaps that extensive focus is causing goals for machine learning and AI to go astray. According to Luis, so much focus is put into obtaining data that we judge how good a machine learning system is by how much data was collected, how large the neural network is, and how much training data was used. When you collect a lot of data you are using that data to build systems that are primarily driven by statistics. Luis says that we latch onto statistics when we feed AI so much data, and that we ascribe to systems intelligence, when in reality, all we have done is created large probabilistic systems that by virtue of large data sets exhibit things we ascribe to intelligence. He says that when our systems arent learning as we want, the primary gut reaction is to give these AI system more data so that we dont have to think as much about the hard parts about generalization and intelligence.

Many would argue that there are some areas where you do need data to help teach AI. Computers are better able to learn image recognition and similar tasks by having more data. The more data, the better the networks, and the more accurate the results. On the podcast, Luis asked whether deep learning is great enough that this works or if we have a big enough data set that image recognition now works. Basically: is it the algorithm or just the sheer quantity of data that is making this work?

Rather, what Luis argues is that if we can find a better way to structure the system as a whole, then the AI system should be able to reason through problems, even with very limited data. Luis compares using machine learning in every application to the retail world. He talks about how physical stores are seeing the success in online stores and trying to copy on that success. One of the ways they are doing this is by using apps to navigate stores. Luis mentioned that he visited a Target where he had to use his phone to navigate the store which was harder than being able to look at signs. Having a human to ask questions and talk to is both faster and part of the experience of being in a brick and mortar retail location. Luis says he would much rather have a human to interact with at one of these locations than a computer.

Is the problem deep learning?

He compares this to machine learning by saying that machine learning has a very narrow application. If you try to apply machine learning to every aspect of AI that you will end up with issues like he did at the Target. Basically looking at neural networks as a hammer and every AI problem as a nail. No one technology or solution works for every application. Perhaps deep learning only works because of vast quantities of data? Maybe theres a better algorithm that can generalize better, apply knowledge learned in one domain to another better, and use smaller amounts of data to get much better quality insights.

People have tried recently to automate many of the jobs that people do. Throughout history, Luis says that technology has killed businesses when it tries to replace humans. Technology and businesses are successful when they expand on what humans can do. Attempting to replace humans is a difficult task and one that is going to lead companies down the road to failure. As humans, he points out, we crave human interaction. Even the age that is constantly on their technology desires human interaction greatly.

Luis also makes a point that while many people mistakenly confuse automation and AI. Automation is using a computer to carry out specific tasks, it is not the creation of intelligence. This is something that many are mentioning on several occasions. Indeed, its the fear of automation and the fictional superintelligence that has many people worried about AI. Dr. Perez-Breva makes the point that many ascribe to machines human characteristics. But this should not be the case with AI system.

Rather, he sees AI systems more akin to a new species with a different mode of intelligence than humans. His opinion is that researchers are very far from creating an AI that is similar to what you will find in books and movies. He blames movies for giving people the impression of robots (AI) killing people and being dangerous technologies. While there are good robots in movies there are few of them and they get pushed to the side by bad robots. He points out that we need to move away from this pushing images of bad robots. Our focus needs to be on how artificial intelligence can help humans grow. It would be beneficial if the movie-making industry could help with this. As such, AI should be thought of as a new intelligent species were trying to create, not something that is meant to replace us.

A positive AI future

Despite negative images and talk, Luis is sure that artificial intelligence is here to stay. At least for a while. So many companies have made large investments into AI that it would be difficult for them to just stop using them or to stop the development.

As a final question in the interview, Luis was asked where he sees the industry of artificial intelligence going. Prefacing his answer with the fact that based on the earlier discussion people are investing in machine learning and not true artificial intelligence, Luis said that he is happy in the investment that businesses are making in what they call AI. He believes that these investments will help the development of this technology to stay around for a minimum of four years.

Once we can stop comparing humans to artificial intelligence, Luis believes that we will see great advancements in what AI can do. He believes that AI has the power to work alongside humans to unlock knowledge and tasks that we werent previously able to do. The point when this happens, he doesnt believe is that far away. We are getting closer to it every day.

Many of Luiss ideas are contrary to popular beliefs by many people who are interested in the world of artificial intelligence. At the same time, these ideas that he presents are presented in a very logical manner and are very thought-provoking. The only way that we will be able to see what is right or where his ideas go is time.

Go here to see the original:
This AI Researcher Thinks We Have It All Wrong - Forbes

AI and machine learning have something of an image problem.

Theyve never been quite so widely discussed as topics, or, arguably, their potential so widely debated. This is, to some extent, part of the problem. Artificial Intelligence can, still, be anything, achieve anything. But until its results are put into practice for people, it remains a misunderstood concept, especially to the layperson.

While well-established industry thought leaders are rightly championing the fact that AI has the potential to be transformative and capable of a wide range of solutions, the lack of context for most people is fuelling fears that it is simply going to replace peoples roles and take over tasks, wholesale. It also ignores the fact that AI applications have been quietly assisting peoples jobs, in a light touch manner, for some time now and people are still in those roles.

Many people are imagining AI to be something it is not. Given the technology is still in a fast-development phase, some people think it is helpful to consider the tech as a type of plug and play, black box technology. Some believe this helps people to put it into the context of how it will work and what it will deliver for businesses. In our opinion, this limits a true understanding of its potential and what it could be delivering for companies day in, day out.

The hyperbole is also not helping. The statements we use AI and our products AI driven have already become well-worn by enthusiastic salespeople and marketeers. While theres a great sales case to be made by that exciting assertion, its rarely speaking the truth about the situation. What is really meant by the current use of artificial intelligence? Arguably, AI is not yet a thing in its own right; i.e the capability of machines to be able to do the things which people do instinctively, which machines instinctively do not. Instead of being excited by hearing the phrase we do AI!, people should see it as a red flag to dig deeper into the technology and the AI capability in question.

SEE ALSO:

Machine learning, similarly, doesnt benefit from sci-fi associations or big sales patter bravado. In its simplest form, while machine learning sounds like a defined and independent process, it is actually a technique to deliver AI functions. Its maths, essentially, applied alongside data, processing power and technology to deliver an AI capability. Machine learning models dont execute actions or do anything themselves, unless people put them to use. They are still human tools, to be deployed by someone to undertake a specific action.

The tools and models are only as good as the human knowledge and skills programming them. People, especially in the legal sectors autologyx works with, are smart, adaptable and vastly knowledgeable. They can quickly shift from one case to another, and have their own methods and processes of approaching problem solving in the workplace. Where AI is coming in to lift the load is on lengthy, detailed, and highly repetitive tasks such as contract renewals. Humans can get understandably bored when reviewing highly repetitive, vast volumes of contracts to change just a few clauses and update the document. A machine learning solution does notnget bored, and performs consistently with a high degree of accuracy, freeing those legal teams up to work on more interesting, varied, or complicated casework.

Together, AI, machine learning and automation are the arms and armour businesses across a range of sectors need to acquire to adapt and continue to compete in the future. The future of the legal industry, for instance, is still a human one where knowledge of people will continue to be an asset. AI in that sector is more focused on codifying and leveraging that intelligence and while the machine and AI models learn and grow from people, so those people will continue to grow and expand their knowledge within the sector too. Today, AI and ML technologies are only as good as the people power programming them.

As Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence put it, AI is neither good nor evil. Its a tool. A technology for us to use. How we choose to apply it is entirely up to us.

By Ben Stoneham, founder and CEO, autologyx

Continue reading here:
Removing the robot factor from AI - Gigabit Magazine - Technology News, Magazine and Website

Editors Note: This article was submitted in response to thecall for ideas issued by the co-chairs of the National Security Commission on Artificial Intelligence, Eric Schmidt and Robert Work. It addresses the third question (part a.), which asks how institutions, organizational structures, and infrastructure will affect AI development, and will artificial intelligence require the development of new institutions or changes to existing institutions.

Artificial intelligence (AI) is often portrayed as a single omnipotent force the computer as God. Often the AI is evil, or at least misguided. According to Hollywood, humans can outwit the computer (2001: A Space Odyssey), reason with it (Wargames), blow it up (Star Wars: The Phantom Menace), or be defeated by it (Dr. Strangelove). Sometimes the AI is an automated version of a human, perhaps a human fighters faithful companion (the robot R2-D2 in Star Wars).

These science fiction tropes are legitimate models for military discussion and many are being discussed. But there are other possibilities. In particular, machine learning may give rise to new forms of intelligence; not natural, but not really artificial if the term implies having been designed in detail by a person. Such new forms of intelligence may resemble that of humans or other animals, and we will discuss them using language associated with humans, but we are not discussing robots that have been deliberately programmed to emulate human intelligence. Through machine learning they have been programmed by their own experiences. We speculate that some of the characteristics that humans have evolved over millennia will also evolve in future AI, characteristics that have evolved purely for their success in a wide range of situations that are real, for humans, or simulated, for robots.

As the capabilities of AI-enabled robots increase, and in particular as behaviors emerge that are both complex and outside past human experience, how will we organize, train, and command them and the humans who will supervise and maintain them? Existing methods and structures, such as military ranks and doctrine, that have evolved over millennia to manage the complexity of human behavior will likely be necessary. But because robots will evolve new behaviors we cannot yet imagine, they are unlikely to be sufficient. Instead, the military and its partners will need to learn new types of organization and new approaches to training. It is impossible to predict what these will be but very possible they will differ greatly from approaches that have worked in the past. Ongoing experimentation will be essential.

How to Respond to AI Advances

The development of AI, especially machine learning, will lead to unpredictable new types of robots. Advances in AI suggest that humans will have the ability to create many types of robots, of different shapes, sizes, or degrees of independence or autonomy. It is conceivable that humans may one day be able to design tiny AI bullets to pierce only designated targets, automated aircraft to fly as loyal wingmen alongside human pilots, or thousands of AI fish to swim up an enemys river. Or we could design AI not as a device but as a global grid that analyzes vast amounts of diverse data. Multiple programs funded by the Department of Defense are on their way to developing robots with varying degrees of autonomy.

In science fiction, robots are often depicted as behaving in groups (like the robot dogs in Metalhead). Researchers inspired by animal behaviors have developed AI concepts such as swarms, in which relatively simple rules for each robot can result in complex emergent phenomena on a larger scale. This is a legitimate and important area of investigation. Nevertheless, simply imitating the known behaviors of animals has its limits. After observing the genocidal nature of military operations among ants, biologists Bert Holldobler and E. O. Wilson wrote, If ants had nuclear weapons, they would probably end the world in a week. Nor would we want to limit AI to imitating human behavior. In any case, a major point of machine learning is the possibility of uncovering new behaviors or strategies. Some of these will be very different from all past experience; human, animal, and automated. We will likely encounter behaviors that, although not human, are so complex that some human language, such as personality, may seem appropriately descriptive. Robots with new, sophisticated patterns of behavior may require new forms of organization.

Military structure and scheme of maneuver is key to victory. Groups often fight best when they dont simply swarm but execute sophisticated maneuvers in hierarchical structures. Modern military tactics were honed over centuries of experimentation and testing. This was a lengthy, expensive, and bloody process.

The development of appropriate organizations and tactics for AI systems will also likely be expensive, although one can hope that through the use of simulation it will not be bloody. But it may happen quickly. The competitive international environment creates pressure to use machine learning to develop AI organizational structure and tactics, techniques, and procedures as fast as possible.

Despite our considerable experience organizing humans, when dealing with robots with new, unfamiliar, and likely rapidly-evolving personalities we confront something of a blank slate. But we must think beyond established paradigms, beyond the computer as all-powerful or the computer as loyal sidekick.

Humans fight in a hierarchy of groups, each soldier in a squad or each battalion in a brigade exercising a combination of obedience and autonomy. Decisions are constantly made at all levels of the organization. Deciding what decisions can be made at what levels is itself an important decision. In an effective organization, decision-makers at all levels have a good idea of how others will act, even when direct communication is not possible.

Imagine an operation in which several hundred underwater robots are swimming up a river to accomplish a mission. They are spotted and attacked. A decision must be made: Should they retreat? Who decides? Communications will likely be imperfect. Some mid-level commander, likely one of the robot swimmers, will decide based on limited information. The decision will likely be difficult and depend on the intelligence, experience, and judgment of the robot commander. It is essential that the swimmers know who or what is issuing legitimate orders. That is, there will have to be some structure, some hierarchy.

The optimal unit structure will be worked out through experience. Achieving as much experience as possible in peacetime is essential. That means training.

Training Robot Warriors

Robots with AI-enabled technologies will have to be exercised regularly, partly to test them and understand their capabilities and partly to provide them with the opportunity to learn from recreating combat. This doesnt mean that each individual hardware item has to be trained, but that the software has to develop by learning from its mistakes in virtual testbeds and, to the extent that they are feasible, realistic field tests. People learn best from the most realistic training possible. There is no reason to expect machines to be any different in that regard. Furthermore, as capabilities, threats, and missions evolve, robots will need to be continuously trained and tested to maintain effectiveness.

Training may seem a strange word for machine learning in a simulated operational environment. But then, conventional training is human learning in a controlled environment. Robots, like humans, will need to learn what to expect from their comrades. And as they train and learn highly complex patterns, it may make sense to think of such patterns as personalities and memories. At least, the patterns may appear that way to the humans interacting with them. The point of such anthropomorphic language is not that the machines have become human, but that their complexity is such that it is helpful to think in these terms.

One big difference between people and machines is that, in theory at least, the products of machine learning, the code for these memories or personalities, can be uploaded directly from one very experienced robot to any number of others. If all robots are given identical training and the same coded memories, we might end up with a uniformity among a units members that, in the aggregate, is less than optimal for the unit as a whole.

Diversity of perspective is accepted as a valuable aid to human teamwork. Groupthink is widely understood to be a threat. Its reasonable to assume that diversity will also be beneficial to teams of robots. It may be desirable to create a library of many different personalities or memories that could be assigned to different robots for particular missions. Different personalities could be deliberately created by using somewhat different sets of training testbeds to develop software for the same mission.

If AI can create autonomous robots with human-like characteristics, what is the ideal personality mix for each mission? Again, we are using the anthropomorphic term personality for the details of the robots behavior patterns. One could call it a robots programming if that did not suggest the existence of an intentional programmer. The robots personalities have evolved from the robots participation in a very large number of simulations. It is unlikely that any human will fully understand a given personality or be able to fully predict all aspects of a robots behavior.

In a simple case, there may be one optimum personality for all the robots of one type. In more complicated situations, where robots will interact with each other, having robots that respond differently to the same stimuli could make a unit more robust. These are things that military planners can hope to learn through testing and training. Of course, attributes of personality that may have evolved for one set of situations may be less than optimal, or positively dangerous, in another. We talk a lot about artificial intelligence. We dont discuss artificial mental illness. But there is no reason to rule it out.

Of course, humans will need to be trained to interact with the machines. Machine learning systems already often exhibit sophisticated behaviors that are difficult to describe. Its unclear how future AI-enabled robots will behave in combat. Humans, and other robots, will need experience to know what to expect and to deal with any unexpected behaviors that may emerge. Planners need experience to know which plans might work.

But the human-robot relationship might turn out to be something completely different. For all of human history, generals have had to learn their soldiers capabilities. They knew best exactly what their troops could do. They could judge the psychological state of their subordinates. They might even know when they were being lied to. But todays commanders do not know, yet, what their AI might prove capable of. In a sense, it is the AI troops that will have to train their commanders.

In traditional military services, the primary peacetime occupation of the combat unit is training. Every single servicemember has to be trained up to the standard necessary for wartime proficiency. This is a huge task. In a robot unit, planners, maintainers, and logisticians will have to be trained to train and maintain the machines but may spend little time working on their hardware except during deployment.

What would the units look like? What is the optimal unit rank structure? How does the human rank structure relate to the robot rank structure? There are a million questions as we enter uncharted territory. The way to find out is to put robot units out onto test ranges where they can operate continuously, test software, and improve machine learning. AI units working together can learn and teach each other and humans.

Conclusion

AI-enabled robots will need to be organized, trained, and maintained. While these systems will have human-like characteristics, they will likely develop distinct personalities. The military will need an extensive training program to inform new doctrines and concepts to manage this powerful, but unprecedented, capability.

Its unclear what structures will prove effective to manage AI robots. Only by continuous experimentation can people, including computer scientists and military operators, understand the developing world of multi-unit human and robot forces. We must hope that experiments lead to correct solutions. There is no guarantee that we will get it right. But there is every reason to believe that as technology enables the development of new and more complex patterns of robot behavior, new types of military organizations will emerge.

Thomas Hamilton is a Senior Physical Scientist at the nonprofit, nonpartisan RAND Corporation. He has a Ph.D. in physics from Columbia University and was a research astrophysicist at Harvard, Columbia, and Caltech before joining RAND. At RAND he has worked extensively on the employment of unmanned air vehicles and other technology issues for the Defense Department.

Image: Wikicommons (U.S. Air Force photo by Kevin L. Moses Sr.)

Read more here:
How to Train Your AI Soldier Robots (and the Humans Who Command Them) - War on the Rocks

Of the many ingredients that go into quality healthcare, comprehensive patient data is close to the top of the list. No one knows this more than Mayur Saxena, CEO and founder of Droice Labs. Saxena created his startup while he was pursuing his doctorate degree at Columbia University, and working at healthcare company conducting clinical trials on new medication. Hes energized by the plethora of opportunities to improve healthcare using artificial intelligence (AI) and machine learning.

Mayur Saxena, CEO and founder of Droice Labs, is energized by the plethora of opportunities to improve healthcare using artificial intelligence (AI) and machine learning.

Patient data is notoriously disorganized and complex, he said. With machine learning, healthcare professionals can organize that information to better understand the disease of every patient and reach them faster with interventions that improve their lives. Its an amazing feeling when you talk with someone whos recovered from an illness because they received the right care.

The idea behind Droice is to make messy data neat, so people can spend less time organizing it and more time analyzing it.

Insights drive personalized patient care

The startup has collected data from 50 million patients in working with healthcare providers, payors, and government organizations in the U.S. and Europe. Healthcare professionals in hospitals, pharmaceutical firms, medical device manufacturing, and insurance rely on Droice Labs natural language understanding (NLU) technology. NLU make sense of patient information in multiple languages from anywhere such as electronic medical records (EMR), insurance claims, research reports, and medical devices.

Our machine learning system takes all the data about an individual into account, and breaks it down so that a doctor, pharmaceutical scientist or healthcare insurer can understand patients better and faster, said Saxena. Instead of repetitive, disparate one-on-one diagnoses and follow-up care, were automating personalized care for a much larger patient population. With shared insights across a large patient population, physicians can chart disease progress and prescribe the best treatment plan. Clinical research into new drugs that took years could be reduced to days or weeks.

Saxena said that one hospital reduced the amount of time it took to arrive at an appropriate diagnosis for patients by over 20 percent.

SAP.iO Foundry opens up world of healthcare opportunities

Droice Labs recently participated in the latest healthcare-focused accelerator program at SAP.iO Foundry New York. It was one of seven up and coming startups working with hospital system providers, employee health and wellness solutions, medical devices, and health IT.

Weve learned so much about customers in the healthcare industry from SAPs sales and product teams, said Saxena. These large organizations have unique needs, and were grateful for the opportunity to partner with SAP, a company with a massive presence across so many geographies. Weve gained valuable insights about strategic global selling and scaling our technology to meet the unique requirements of these customers.

The Droice Labs machine learning platform is now downloadable on the SAP App Center.

Turning long-time passion into thriving startup

Droice Labs reflects Saxenas long-time personal and career commitment to healthcare. After earning his undergraduate degree in bioengineering and biomedical engineering, he worked in high-performance computing in Singapore before arriving in the United States. Thats when he acted on his passion, exploring how AI and machine learning can help improve patient care, and potentially eradicate disease.

Were looking at data from hundreds of thousands of patients a day, helping improve their care pathways across the healthcare system, said Saxena. We have the technology to work with patient data at scale. Im most excited about working together with recognized healthcare experts using state-of-the-art technology to address major challenges in this complicated, regulated industry.

Digitally trustworthy strategy

In an environment where patient concerns and regulations around data control continue to increase, Saxena emphasized his companys strategy of digital trust.

Everything we do is designed to respect individual patient privacy, he said. We dont possess related identifying data on patients, and we remove any identifiers. Working in a mission critical environment like healthcare brings a set of responsibilities. If there is a population suffering from disease, and by looking at their information we can partner with healthcare providers to help make their quality of life better, thats what well do. But we dont participate in business models targeted to specific individuals.

Saxena expected his companys rapid growth trajectory to continue, and it was easy to see why. According to Gartners 2020 CIO Survey, AI is the healthcare industrys top game-changing technology. These analysts predicted 75 percent ofhealthcare delivery organizationswill invest in an AI capability to explicitly improve either operational performance or clinical outcomes by 2021.

Read this article:
AI Is Top Game-Changing Technology In Healthcare Industry - Forbes

In the summer of 1955, while planning a now famous workshop at Dartmouth College, John McCarthy coined the term artificial intelligence to describe a new field of computer science. Rather than writing programs that tell a computer how to carry out a specific task, McCarthy pledged that he and his colleagues would instead pursue algorithms that could teach themselves how to do so. The goal was to create computers that could observe the world and then make decisions based on those observationsto demonstrate, that is, an innate intelligence.

The question was how to achieve that goal. Early efforts focused primarily on whats known as symbolic AI, which tried to teach computers how to reason abstractly. But today the dominant approach by far is machine learning, which relies on statistics instead. Although the approach dates back to the 1950sone of the attendees at Dartmouth, Arthur Samuels, was the first to describe his work as machine learningit wasnt until the past few decades that computers had enough storage and processing power for the approach to work well. The rise of cloud computing and customized chips has powered breakthrough after breakthrough, with research centers like OpenAI or DeepMind announcing stunning new advances seemingly every week.

Machine learning is now so popular that it has effectively become synonymous with artificial intelligence itself. As a result, its not possible to tease out the implications of AI without understanding how machine learning works.

The extraordinary success of machine learning has made it the default method of choice for AI researchers and experts. Indeed, machine learning is now so popular that it has effectively become synonymous with artificial intelligence itself. As a result, its not possible to tease out the implications of AI without understanding how machine learning worksas well as how it doesnt.

The core insight of machine learning is that much of what we recognize as intelligence hinges on probability rather than reason or logic.If you think about it long enough, this makes sense. When we look at a picture of someone, our brains unconsciously estimate how likely it is that we have seen their face before. When we drive to the store, we estimate which route is most likely to get us there the fastest. When we play a board game, we estimate which move is most likely to lead to victory. Recognizing someone, planning a trip, plotting a strategyeach of these tasks demonstrate intelligence. But rather than hinging primarily on our ability to reason abstractly or think grand thoughts, they depend first and foremost on our ability to accurately assess how likely something is. We just dont always realize that thats what were doing.

Back in the 1950s, though, McCarthy and his colleagues did realize it. And they understood something else too: Computers should be very good at computing probabilities. Transistors had only just been invented, and had yet to fully supplant vacuum tube technology. But it was clear even then that with enough data, digital computers would be ideal for estimating a given probability. Unfortunately for the first AI researchers, their timing was a bit off. But their intuition was spot onand much of what we now know as AI is owed to it. When Facebook recognizes your face in a photo, or Amazon Echo understands your question, theyre relying on an insight that is over sixty years old.

The core insight of machine learning is that much of what we recognize as intelligence hinges on probability rather than reason or logic.

The machine learning algorithm that Facebook, Google, and others all use is something called a deep neural network. Building on the prior work of Warren McCullough and Walter Pitts, Frank Rosenblatt coded one of the first working neural networks in the late 1950s. Although todays neural networks are a bit more complex, the main idea is still the same: The best way to estimate a given probability is to break the problem down into discrete, bite-sized chunks of information, or what McCullough and Pitts termed a neuron. Their hunch was that if you linked a bunch of neurons together in the right way, loosely akin to how neurons are linked in the brain, then you should be able to build models that can learn a variety of tasks.

To get a feel for how neural networks work, imagine you wanted to build an algorithm to detect whether an image contained a human face. A basic deep neural network would have several layers of thousands of neurons each. In the first layer, each neuron might learn to look for one basic shape, like a curve or a line. In the second layer, each neuron would look at the first layer, and learn to see whether the lines and curves it detects ever make up more advanced shapes, like a corner or a circle. In the third layer, neurons would look for even more advanced patterns, like a dark circle inside a white circle, as happens in the human eye. In the final layer, each neuron would learn to look for still more advanced shapes, such as two eyes and a nose. Based on what the neurons in the final layer say, the algorithm will then estimate how likely it is that an image contains a face. (For an illustration of how deep neural networks learn hierarchical feature representations, see here.)

The magic of deep learning is that the algorithm learns to do all this on its own. The only thing a researcher does is feed the algorithm a bunch of images and specify a few key parameters, like how many layers to use and how many neurons should be in each layer, and the algorithm does the rest. At each pass through the data, the algorithm makes an educated guess about what type of information each neuron should look for, and then updates each guess based on how well it works. As the algorithm does this over and over, eventually it learns what information to look for, and in what order, to best estimate, say, how likely an image is to contain a face.

Whats remarkable about deep learning is just how flexible it is. Although there are other prominent machine learning algorithms tooalbeit with clunkier names, like gradient boosting machinesnone are nearly so effective across nearly so many domains. With enough data, deep neural networks will almost always do the best job at estimating how likely something is. As a result, theyre often also the best at mimicking intelligence too.

Yet as with machine learning more generally, deep neural networks are not without limitations. To build their models, machine learning algorithms rely entirely on training data, which means both that they will reproduce the biases in that data, and that they will struggle with cases that are not found in that data. Further, machine learning algorithms can also be gamed. If an algorithm is reverse engineered, it can be deliberately tricked into thinking that, say, a stop sign is actually a person. Some of these limitations may be resolved with better data and algorithms, but others may be endemic to statistical modeling.

To glimpse how the strengths and weaknesses of AI will play out in the real-world, it is necessary to describe the current state of the art across a variety of intelligent tasks. Below, I look at the situation in regard to speech recognition, image recognition, robotics, and reasoning in general.

Ever since digital computers were invented, linguists and computer scientists have sought to use them to recognize speech and text. Known as natural language processing, or NLP, the field once focused on hardwiring syntax and grammar into code. However, over the past several decades, machine learning has largely surpassed rule-based systems, thanks to everything from support vector machines to hidden markov models to, most recently, deep learning. Apples Siri, Amazons Alexa, and Googles Duplex all rely heavily on deep learning to recognize speech or text, and represent the cutting-edge of the field.

When several leading researchers recently set a deep learning algorithm loose on Amazon reviews, they were surprised to learn that the algorithm had not only taught itself grammar and syntax, but a sentiment classifier too.

The specific deep learning algorithms at play have varied somewhat. Recurrent neural networks powered many of the initial deep learning breakthroughs, while hierarchical attention networks are responsible for more recent ones. What they all share in common, though, is that the higher levels of a deep learning network effectively learn grammar and syntax on their own. In fact, when several leading researchers recently set a deep learning algorithm loose on Amazon reviews, they were surprised to learn that the algorithm had not only taught itself grammar and syntax, but a sentiment classifier too.

Yet for all the success of deep learning at speech recognition, key limitations remain. The most important is that because deep neural networks only ever build probabilistic models, they dont understand language in the way humans do; they can recognize that the sequence of letters k-i-n-g and q-u-e-e-n are statistically related, but they have no innate understanding of what either word means, much less the broader concepts of royalty and gender. As a result, there is likely to be a ceiling to how intelligent speech recognition systems based on deep learning and other probabilistic models can ever be. If we ever build an AI like the one in the movie Her, which was capable of genuine human relationships, it will almost certainly take a breakthrough well beyond what a deep neural network can deliver.

When Rosenblatt first implemented his neural network in 1958, he initially set it loose onimages of dogs and cats. AI researchers have been focused on tackling image recognition ever since. By necessity, much of that time was spent devising algorithms that could detect pre-specified shapes in an image, like edges and polyhedrons, using the limited processing power of early computers. Thanks to modern hardware, however, the field of computer vision is now dominated by deep learning instead. When a Tesla drives safely in autopilot mode, or when Googles new augmented-reality microscope detects cancer in real-time, its because of a deep learning algorithm.

A few stickers on a stop sign can be enough to prevent a deep learning model from recognizing it as such. For image recognition algorithms to reach their full potential, theyll need to become much more robust.

Convolutional neural networks, or CNNs, are the variant of deep learning most responsible for recent advances in computer vision. Developed by Yann LeCun and others, CNNs dont try to understand an entire image all at once, but instead scan it in localized regions, much the way a visual cortex does. LeCuns early CNNs were used to recognize handwritten numbers, but today the most advanced CNNs, such as capsule networks, can recognize complex three-dimensional objects from multiple angles, even those not represented in training data. Meanwhile, generative adversarial networks, the algorithm behind deep fake videos, typically use CNNs not to recognize specific objects in an image, but instead to generate them.

As with speech recognition, cutting-edge image recognition algorithms are not without drawbacks. Most importantly, just as all that NLP algorithms learn are statistical relationships between words, all that computer vision algorithms learn are statistical relationships between pixels. As a result, they can be relatively brittle. A few stickers on a stop sign can be enough to prevent a deep learning model from recognizing it as such. For image recognition algorithms to reach their full potential, theyll need to become much more robust.

What makes our intelligence so powerful is not just that we can understand the world, but that we can interact with it. The same will be true for machines. Computers that can learn to recognize sights and sounds are one thing; those that can learn to identify an object as well as how to manipulate it are another altogether. Yet if image and speech recognition are difficult challenges, touch and motor control are far more so. For all their processing power, computers are still remarkably poor at something as simple as picking up a shirt.

The reason: Picking up an object like a shirt isnt just one task, but several. First you need to recognize a shirt as a shirt. Then you need to estimate how heavy it is, how its mass is distributed, and how much friction its surface has. Based on those guesses, then you need to estimate where to grasp the shirt and how much force to apply at each point of your grip, a task made all the more challenging because the shirts shape and distribution of mass will change as you lift it up. A human does this trivially and easily. But for a computer, the uncertainty in any of those calculations compounds across all of them, making it an exceedingly difficult task.

Initially, programmers tried to solve the problem by writing programs that instructed robotic arms how to carry out each task step by step. However, just as rule-based NLP cant account for all possible permutations of language, there also is no way for rule-based robotics to run through all the possible permutations of how an object might be grasped. By the 1980s, it became increasingly clear that robots would need to learn about the world on their own and develop their own intuitions about how to interact with it. Otherwise, there was no way they would be able to reliably complete basic maneuvers like identifying an object, moving toward it, and picking it up.

The current state of the art is something called deep reinforcement learning. As a crude shorthand, you can think of reinforcement learning as trial and error. If a robotic arm tries a new way of picking up an object and succeeds, it rewards itself; if it drops the object, it punishes itself. The more the arm attempts its task, the better it gets at learning good rules of thumb for how to complete it. Coupled with modern computing, deep reinforcement learning has shown enormous promise. For instance, by simulating a variety of robotic hands across thousands of servers, OpenAI recently taught a real robotic hand how to manipulate a cube marked with letters.

For all their processing power, computers are still remarkably poor at something as simple as picking up a shirt.

Compared with prior research, OpenAIs breakthrough is tremendously impressive. Yet it also shows the limitations of the field. The hand OpenAI built didnt actually feel the cube at all, but instead relied on a camera. For an object like a cube, which doesnt change shape and can be easily simulated in virtual environments, such an approach can work well. But ultimately, robots will need to rely on more than just eyes. Machines with the dexterity and fine motor skills of a human are still a ways away.

When Arthur Samuels coined the term machine learning, he wasnt researching image or speech recognition, nor was he working on robots. Instead, Samuels was tackling one of his favorite pastimes: checkers. Since the game had far too many potential board moves for a rule-based algorithm to encode them all, Samuels devised an algorithm that could teach itself to efficiently look several moves ahead. The algorithm was noteworthy for working at all, much less being competitive with other humans. But it also anticipated the astonishing breakthroughs of more recent algorithms like AlphaGo and AlphaGo Zero, which have surpassed all human players at Go, widely regarded as the most intellectually demanding board game in the world.

As with robotics, the best strategic AI relies on deep reinforcement learning. In fact, the algorithm that OpenAI used to power its robotic hand also formed the core of its algorithm for playing Dota 2, a multi-player video game. Although motor control and gameplay may seem very different, both involve the same process: making a sequence of moves over time, and then evaluating whether they led to success or failure. Trial and error, it turns out, is as useful for learning to reason about a game as it is for manipulating a cube.

Since the algorithm works only by learning from outcome data, it needs a human to define what the outcome should be. As a result, reinforcement learning is of little use in the many strategic contexts in which the outcome is not always clear.

From Samuels on, the success of computers at board games has posed a puzzle to AI optimists and pessimists alike. If a computer can beat a human at a strategic game like chess, how much can we infer about its ability to reason strategically in other environments? For a long time, the answer was, very little. After all, most board games involve a single player on each side, each with full information about the game, and a clearly preferred outcome. Yet most strategic thinking involves cases where there are multiple players on each side, most or all players have only limited information about what is happening, and the preferred outcome is not clear. For all of AlphaGos brilliance, youll note that Google didnt then promote it to CEO, a role that is inherently collaborative and requires a knack for making decisions with incomplete information.

Fortunately, reinforcement learning researchers have recently made progress on both of those fronts. One team outperformed human players at Texas Hold Em, a poker game where making the most of limited information is key. Meanwhile, OpenAIs Dota 2 player, which coupled reinforcement learning with whats called a Long Short-Term Memory (LSTM) algorithm, has made headlines for learning how to coordinate the behavior of five separate bots so well that they were able to beat a team of professional Dota 2 players. As the algorithms improve, humans will likely have a lot to learn about optimal strategies for cooperation, especially in information-poor environments.This kind of information would be especially valuable for commanders in military settings, who sometimes have to make decisions without having comprehensive information.

Yet theres still one challenge no reinforcement learning algorithm can ever solve. Since the algorithm works only by learning from outcome data, it needs a human to define what the outcome should be. As a result, reinforcement learning is of little use in the many strategic contexts in which the outcome is not always clear. Should corporate strategy prioritize growth or sustainability? Should U.S. foreign policy prioritize security or economic development? No AI will ever be able to answer higher-order strategic reasoning, because, ultimately, those are moral or political questions rather than empirical ones. The Pentagon may lean more heavily on AI in the years to come, but it wont be taking over the situation room and automating complex tradeoffs any time soon.

From autonomous cars to multiplayer games, machine learning algorithms can now approach or exceed human intelligence across a remarkable number of tasks. The breakout success of deep learning in particular has led to breathless speculation about both the imminent doom of humanity and its impending techno-liberation. Not surprisingly, all the hype has led several luminaries in the field, such as Gary Marcus or Judea Pearl, to caution that machine learning is nowhere near as intelligent as it is being presented, or that perhaps we should defer our deepest hopes and fears about AI until it is based on more than mere statistical correlations. Even Geoffrey Hinton, a researcher at Google and one of the godfathers of modern neural networks, has suggested that deep learning alone is unlikely to deliver the level of competence many AI evangelists envision.

Where the long-term implications of AI are concerned, the key question about machine learning is this: How much of human intelligence can be approximated with statistics? If all of it can be, then machine learning may well be all we need to get to a true artificial general intelligence. But its very unclear whether thats the case. As far back as 1969, when Marvin Minsky and Seymour Papert famously argued that neural networks had fundamental limitations, even leading experts in AI have expressed skepticism that machine learning would be enough. Modern skeptics like Marcus and Pearl are only writing the latest chapter in a much older book. And its hard not to find their doubts at least somewhat compelling. The path forward from the deep learning of today, which can mistake a rifle for a helicopter, is by no means obvious.

Where the long-term implications of AI are concerned, the key question about machine learning is this: How much of human intelligence can be approximated with statistics?

Yet the debate over machine learnings long-term ceiling is to some extent beside the point. Even if all research on machine learning were to cease, the state-of-the-art algorithms of today would still have an unprecedented impact. The advances that have already been made in computer vision, speech recognition, robotics, and reasoning will be enough to dramatically reshape our world. Just as happened in the so-called Cambrian explosion, when animals simultaneously evolved the ability to see, hear, and move, the coming decade will see an explosion in applications that combine the ability to recognize what is happening in the world with the ability to move and interact with it. Those applications will transform the global economy and politics in ways we can scarcely imagine today. Policymakers need not wring their hands just yet about how intelligent machine learning may one day become. They will have their hands full responding to how intelligent it already is.

Go here to see the original:
What is machine learning? - Brookings

As the fuel that powers their ongoing digital transformation efforts, businesses everywhere are looking for ways to derive as much insight as possible from their data. The accompanying increased demand for advanced predictive and prescriptive analytics has, in turn, led to a call for more data scientists proficient with the latest artificial intelligence (AI) and machine learning (ML) tools.

But such highly-skilled data scientists are expensive and in short supply. In fact, theyre such a precious resource that the phenomenon of the citizen data scientist has recently arisen to help close the skills gap. A complementary role, rather than a direct replacement, citizen data scientists lack specific advanced data science expertise. However, they are capable of generating models using state-of-the-art diagnostic and predictive analytics. And this capability is partly due to the advent of accessible new technologies such as automated machine learning (AutoML) that now automate many of the tasks once performed by data scientists.

Algorithms and automation

According to a recent Harvard Business Review article, Organisations have shifted towards amplifying predictive power by coupling big data with complex automated machine learning. AutoML, which uses machine learning to generate better machine learning, is advertised as affording opportunities to democratise machine learning by allowing firms with limited data science expertise to develop analytical pipelines capable of solving sophisticated business problems.

Comprising a set of algorithms that automate the writing of other ML algorithms, AutoML automates the end-to-end process of applying ML to real-world problems. By way of illustration, a standard ML pipeline is made up of the following: data pre-processing, feature extraction, feature selection, feature engineering, algorithm selection, and hyper-parameter tuning. But the considerable expertise and time it takes to implement these steps means theres a high barrier to entry.

AutoML removes some of these constraints. Not only does it significantly reduce the time it would typically take to implement an ML process under human supervision, it can also often improve the accuracy of the model in comparison to hand-crafted models, trained and deployed by humans. In doing so, it offers organisations a gateway into ML, as well as freeing up the time of ML engineers and data practitioners, allowing them to focus on higher-order challenges.

SEE ALSO:

Overcoming scalability problems

The trend for combining ML with Big Data for advanced data analytics began back in 2012, when deep learning became the dominant approach to solving ML problems. This approach heralded the generation of a wealth of new software, tooling, and techniques that altered both the workload and the workflow associated with ML on a large scale. Entirely new ML toolsets, such as TensorFlow and PyTorch were created, and people increasingly began to engage more with graphics processing units (GPUs) to accelerate their work.

Until this point, companies efforts had been hindered by the scalability problems associated with running ML algorithms on huge datasets. Now, though, they were able to overcome these issues. By quickly developing sophisticated internal tooling capable of building world-class AI applications, the BigTech powerhouses soon overtook their Fortune 500 peers when it came to realising the benefits of smarter data-driven decision-making and applications.

Insight, innovation and data-driven decisions

AutoML represents the next stage in MLs evolution, promising to help non-tech companies access the capabilities they need to quickly and cheaply build ML applications.

In 2018, for example, Google launched its Cloud AutoML. Based on Neural Architecture Search (NAS) and transfer learning, it was described by Google executives as having the potential to make AI experts even more productive, advance new fields in AI, and help less-skilled engineers build powerful AI systems they previously only dreamed of.

The one downside to Googles AutoML is that its a proprietary algorithm. There are, however, a number of alternative open-source AutoML libraries such as AutoKeras, developed by researchers at Texas University and used to power the NAS algorithm.

Technological breakthroughs such as these have given companies the capability to easily build production-ready models without the need for expensive human resources. By leveraging AI, ML, and deep learning capabilities, AutoML gives businesses across all industries the opportunity to benefit from data-driven applications powered by statistical models - even when advanced data science expertise is scarce.

With organisations increasingly reliant on civilian data scientists, 2020 is likely to be the year that enterprise adoption of AutoML will start to become mainstream. Its ease of access will compel business leaders to finally open the black box of ML, thereby elevating their knowledge of its processes and capabilities. AI and ML tools and practices will become ever more ingrained in businesses everyday thinking and operations as they become more empowered to identify those projects whose invaluable insight will drive better decision-making and innovation.

By Senthil Ravindran, EVP and global head of cloud transformation and digital innovation, Virtusa

View original post here:
Why 2020 will be the Year of Automated Machine Learning - Gigabit Magazine - Technology News, Magazine and Website

Highlights:

Recently, the international evaluation agency Standard Performance Evaluation Corporation (SPEC) has finalized the election of new Open System Steering Committee (OSSC) executive members, which include Inspur, Intel, AMD, IBM, Oracle and other three companies.

It is worth noting that Inspur, a re-elected OSSC member, was also re-elected as the chair of the SPEC Machine Learning (SPEC ML) working group. The development plan of ML test benchmark proposed by Inspur has been approved by members which aims to provide users with standard on evaluating machine learning computing performance.

SPEC is a global and authoritative third-party application performance testing organization established in 1988, which aims to establish and maintain a series of performance, function, and energy consumption benchmarks, and provides important reference standards for users to evaluate the performance and energy efficiency of computing systems. The organization consists of 138 well-known technology companies, universities and research institutions in the industry such as Intel, Oracle, NVIDIA, Apple, Microsoft, Inspur, Berkeley, Lawrence Berkeley National Laboratory, etc., and its test standard has become an important indicator for many users to evaluate overall computing performance.

The OSSC executive committee is the permanent body of the SPEC OSG (short for Open System Group, the earliest and largest committee established by SPEC) and is responsible for supervising and reviewing the daily work of major technical groups of OSG, major issues, additions and deletions of members, development direction of research and decision of testing standards, etc. Meanwhile, OSSC executive committee uniformly manages the development and maintenance of SPEC CPU, SPEC Power, SPEC Java, SPEC Virt and other benchmarks.

Machine Learning is an important direction in AI development. Different computing accelerator technologies such as GPU, FPGA, ASIC, and different AI frameworks such as TensorFlow and Pytorch provide customers with a rich marketplace of options. However, the next important thing for the customer to consider is how to evaluate the computing efficiency of various AI computing platforms. Both enterprises and research institutions require a set of benchmarks and methods to effectively measure performance to find the right solution for their needs.

In the past year, Inspur has done much to advance the SPEC ML standard specific component development, contributing test models, architectures, use cases, methods and so on, which have been duly acknowledged by SPEC organization and its members.

Joe Qiao, General Manager of Inspur Solution and Evaluation Department, believes that SPEC ML can provide an objective comparison standard for AI / ML applications, which will help users choose a computing system that best meet their application needs. Meanwhile, it also provides a unified measurement standard for manufacturers to improve their technologies and solution capabilities, advancing the development of the AI industry.

About Inspur

Inspur is a leading provider of data center infrastructure, cloud computing, and AI solutions, ranking among the worlds top 3 server manufacturers. Through engineering and innovation, Inspur delivers cutting-edge computing hardware design and extensive product offerings to address important technology arenas like open computing, cloud data center, AI and deep learning. Performance-optimized and purpose-built, our world-class solutions empower customers to tackle specific workloads and real-world challenges. To learn more, please go to http://www.inspursystems.com.

View source version on businesswire.com: https://www.businesswire.com/news/home/20200221005123/en/

Contacts

Media Fiona LiuLiuxuan01@inspur.com

View original post here:
Inspur Re-Elected as Member of SPEC OSSC and Chair of SPEC Machine Learning - Yahoo Finance