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Computer vision in AI: The data needed to succeed

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Computer vision in AI: The data needed to succeed


Developing the capacity to annotate massive volumes of data while maintaining quality is a function of the model development lifecycle that enterprises often underestimate. It’s resource intensive and requires specialized expertise.

At the heart of any successful machine learning/artificial intelligence (ML/AI) initiative is a commitment to high-quality training data and a pathway to quality data that is proven and well-defined. Without this quality data pipeline, the initiative is doomed to fail.

Computer vision or data science teams often turn to external partners to develop their data training pipeline, and these partnerships drive model performance.

There is no one definition of quality: “quality data” is completely contingent on the specific computer vision or machine learning project. However, there is a general process all teams can follow when working with an external partner, and this path to quality data can be broken down into four prioritized phases.

Annotation criteria and quality requirements

Training data quality is an evaluation of a data set’s fitness to serve its purpose in a given ML/AI use case.

The computer vision team needs to establish an unambiguous set of rules that describe what quality means in the context of their project. Annotation criteria are the collection of rules that define which objects to annotate, how to annotate them correctly, and what the quality targets are.

Accuracy or quality targets define the lowest acceptable result for evaluation metrics like accuracy, recall, precision, F1 score, et cetera. Typically, a computer vision team will have quality targets for how accurately objects of interest were classified, how accurately objects were localized, and how accurately relationships between objects were identified.

Workforce training and platform configuration

Platform configuration. Task design and workflow setup require time and expertise, and accurate annotation requires task-specific tools. At this stage, data science teams need a partner with expertise to help them determine how best to configure labeling tools, classification taxonomies, and annotation interfaces for accuracy and throughput.

Worker testing and scoring. To accurately label data, annotators need a well-designed training curriculum so they fully understand the annotation criteria and domain context. The annotation platform or external partner should ensure accuracy by actively tracking annotator proficiency against gold data tasks or when a judgement is modified by a higher-skilled worker or admin.

Ground truth or gold data. Ground truth data is crucial at this stage of the process as the baseline to score workers and measure output quality. Many computer vision teams are already working with a ground truth data set.

Sources of authority and quality assurance

There is no one-size-fits-all quality assurance (QA) approach that will meet the quality standards of all ML use cases. Specific business objectives, as well as the risk associated with an under-performing model, will drive quality requirements. Some projects reach target quality using multiple annotators. Others require complex reviews against ground truth data or escalation workflows with verification from a subject matter expert.

There are two primary sources of authority that can be used to measure the quality of annotations and that are used to score workers: gold data and expert review.

  • Gold data: The gold data or ground truth set of records can be used both as a qualification tool for testing and scoring workers at the outset of the process and also as the measure for output quality. When you use gold data to measure quality, you compare worker annotations to your expert annotations for the same data set, and the difference between these two independent, blind answers can be used to produce quantitative measurements like accuracy, recall, precision, and F1 scores.
  • Expert review: This method of quality assurance relies on expert review from a highly skilled worker, an admin, or from an expert on the customer side, sometimes all three. It can be used in conjunction with gold data QA. The expert reviewer looks at the answer given by the qualified worker and either approves it or makes corrections as needed, producing a new correct answer. Initially, an expert review may take place for every single instance of labeled data, but over time, as worker quality improves, expert review can utilize random sampling for ongoing quality control.

Iterating on data success

Once a computer vision team has successfully launched a high quality training data pipeline, it can accelerate progress to a production ready model. Through ongoing support, optimization, and quality control, an external partner can help them:

  • Track velocity: In order to scale effectively, it’s good to measure annotation throughput. How long is it taking data to move through the process? Is the process getting faster?
  • Tune worker training: As the project scales, labeling and quality requirements may evolve. This necessitates ongoing workforce training and scoring.
  • Train on edge cases: Over time, training data should include more and more edge cases in order to make your model as accurate and robust as possible.

Without high-quality training data, even the best funded, most ambitious ML/AI projects cannot succeed. Computer vision teams need partners and platforms they can trust to deliver the data quality they need and to power life-changing ML/AI models for the world.

Alegion is the proven partner to build the training data pipeline that will fuel your model throughout its lifecycle. Contact Alegion at [email protected].

This content was produced by Alegion. It was not written by MIT Technology Review’s editorial staff.

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How AI is reinventing what computers are

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How AI is reinventing what computers are


Fall 2021: the season of pumpkins, pecan pies, and peachy new phones. Every year, right on cue, Apple, Samsung, Google, and others drop their latest releases. These fixtures in the consumer tech calendar no longer inspire the surprise and wonder of those heady early days. But behind all the marketing glitz, there’s something remarkable going on. 

Google’s latest offering, the Pixel 6, is the first phone to have a separate chip dedicated to AI that sits alongside its standard processor. And the chip that runs the iPhone has for the last couple of years contained what Apple calls a “neural engine,” also dedicated to AI. Both chips are better suited to the types of computations involved in training and running machine-learning models on our devices, such as the AI that powers your camera. Almost without our noticing, AI has become part of our day-to-day lives. And it’s changing how we think about computing.

What does that mean? Well, computers haven’t changed much in 40 or 50 years. They’re smaller and faster, but they’re still boxes with processors that run instructions from humans. AI changes that on at least three fronts: how computers are made, how they’re programmed, and how they’re used. Ultimately, it will change what they are for. 

“The core of computing is changing from number-crunching to decision-­making,” says Pradeep Dubey, director of the parallel computing lab at Intel. Or, as MIT CSAIL director Daniela Rus puts it, AI is freeing computers from their boxes. 

More haste, less speed

The first change concerns how computers—and the chips that control them—are made. Traditional computing gains came as machines got faster at carrying out one calculation after another. For decades the world benefited from chip speed-ups that came with metronomic regularity as chipmakers kept up with Moore’s Law. 

But the deep-learning models that make current AI applications work require a different approach: they need vast numbers of less precise calculations to be carried out all at the same time. That means a new type of chip is required: one that can move data around as quickly as possible, making sure it’s available when and where it’s needed. When deep learning exploded onto the scene a decade or so ago, there were already specialty computer chips available that were pretty good at this: graphics processing units, or GPUs, which were designed to display an entire screenful of pixels dozens of times a second. 

Anything can become a computer. Indeed, most household objects, from toothbrushes to light switches to doorbells, already come in a smart version.

Now chipmakers like Intel and Arm and Nvidia, which supplied many of the first GPUs, are pivoting to make hardware tailored specifically for AI. Google and Facebook are also forcing their way into this industry for the first time, in a race to find an AI edge through hardware. 

For example, the chip inside the Pixel 6 is a new mobile version of Google’s tensor processing unit, or TPU. Unlike traditional chips, which are geared toward ultrafast, precise calculations, TPUs are designed for the high-volume but low-­precision calculations required by neural networks. Google has used these chips in-house since 2015: they process people’s photos and natural-­language search queries. Google’s sister company DeepMind uses them to train its AIs. 

In the last couple of years, Google has made TPUs available to other companies, and these chips—as well as similar ones being developed by others—are becoming the default inside the world’s data centers. 

AI is even helping to design its own computing infrastructure. In 2020, Google used a reinforcement-­learning algorithm—a type of AI that learns how to solve a task through trial and error—to design the layout of a new TPU. The AI eventually came up with strange new designs that no human would think of—but they worked. This kind of AI could one day develop better, more efficient chips. 

Show, don’t tell

The second change concerns how computers are told what to do. For the past 40 years we have been programming computers; for the next 40 we will be training them, says Chris Bishop, head of Microsoft Research in the UK. 

Traditionally, to get a computer to do something like recognize speech or identify objects in an image, programmers first had to come up with rules for the computer.

With machine learning, programmers no longer write rules. Instead, they create a neural network that learns those rules for itself. It’s a fundamentally different way of thinking. 

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Decarbonizing industries with connectivity and 5G

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Decarbonizing industries with connectivity and 5G


The United Nations Intergovernmental Panel on Climate Change’s sixth climate change report—an aggregated assessment of scientific research prepared by some 300 scientists across 66 countries—has served as the loudest and clearest wake-up call to date on the global warming crisis. The panel unequivocally attributes the increase in the earth’s temperature—it has risen by 1.1 °C since the Industrial Revolution—to human activity. Without substantial and immediate reductions in carbon dioxide and other greenhouse gas emissions, temperatures will rise between 1.5 °C and 2 °C before the end of the century. That, the panel posits, will lead all of humanity to a “greater risk of passing through ‘tipping points,’ thresholds beyond which certain impacts can no longer be avoided even if temperatures are brought back down later on.”

Corporations and industries must therefore redouble their greenhouse gas emissions reduction and removal efforts with speed and precision—but to do this, they must also commit to deep operational and organizational transformation. Cellular infrastructure, particularly 5G, is one of the many digital tools and technology-enabled processes organizations have at their disposal to accelerate decarbonization efforts.  

5G and other cellular technology can enable increasingly interconnected supply chains and networks, improve data sharing, optimize systems, and increase operational efficiency. These capabilities could soon contribute to an exponential acceleration of global efforts to reduce carbon emissions.

Industries such as energy, manufacturing, and transportation could have the biggest impact on decarbonization efforts through the use of 5G, as they are some of the biggest greenhouse-gas-emitting industries, and all rely on connectivity to link to one another through communications network infrastructure.

The higher performance and improved efficiency of 5G—which delivers higher multi-gigabit peak data speeds, ultra-low latency, increased reliability, and increased network capacity—could help businesses and public infrastructure providers focus on business transformation and reduction of harmful emissions. This requires effective digital management and monitoring of distributed operations with resilience and analytic insight. 5G will help factories, logistics networks, power companies, and others operate more efficiently, more consciously, and more purposely in line with their explicit sustainability objectives through better insight and more powerful network configurations.

This report, “Decarbonizing industries with connectivity & 5G,” argues that the capabilities enabled by broadband cellular connectivity primarily, though not exclusively, through 5G network infrastructure are a unique, powerful, and immediate enabler of carbon reduction efforts. They have the potential to create a transformational acceleration of decarbonization efforts, as increasingly interconnected supply chains, transportation, and energy networks share data to increase efficiency and productivity, hence optimizing systems for lower carbon emissions.

Explore more.

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Surgeons have successfully tested a pig’s kidney in a human patient

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Surgeons have successfully tested a pig’s kidney in a human patient


The reception: The research was conducted last month and is yet to be peer reviewed or published in a journal, but external experts say it represents a major advance. “There is no doubt that this is a highly significant breakthrough,” says Darren K. Griffin, a professor of genetics at the University of Kent, UK. “The research team were cautious, using a patient who had suffered brain death, attaching the kidney to the outside of the body, and closely monitoring for only a limited amount of time. There is thus a long way to go and much to discover,” he added. 

“This is a huge breakthrough. It’s a big, big deal,” Dorry Segev, a professor of transplant surgery at Johns Hopkins School of Medicine who was not involved in the research, told the New York Times. However, he added, “we need to know more about the longevity of the organ.”

The background: In recent years, research has increasingly zeroed in on pigs as the most promising avenue to help address the shortage of organs for transplant, but it has faced a number of obstacles, most prominently the fact that a sugar in pig cells triggers an aggressive rejection response in humans.

The researchers got around this by genetically altering the donor pig to knock out the gene encoding the sugar molecule that causes the rejection response. The pig was genetically engineered by Revivicor, one of several biotech companies working to develop pig organs to transplant into humans. 

The big prize: There is a dire need for more kidneys. More than 100,000 people in the US are currently waiting for a kidney transplant, and 13 die of them every day, according to the National Kidney Foundation. Genetically engineered pigs could offer a crucial lifeline for these people, if the approach tested at NYU Langone can work for much longer periods.

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