Starting with security, Google says that its latest machine learning (ML) model has enabled Chrome to detect over twice as many phishing attacks and malicious sites.

The new on-device machine learning model was rolled out in March. Since its rollout, Google claims that Chrome has detected 2.5x more threats.

Beyond security, Google is also preparing to use machine learning to improve the experience of Chrome users.

Chrome enables users to reject notifications from pages they don’t care about. In the next release of Chrome, Google says it intends to implement an AI model that learns when users are unlikely to grant prompts based on previous interactions and will silence them to minimise interruptions.

This is how a website that’s had its notifications blocked will look:

The design ensures that users aren’t interrupted but can enable notifications if the ML model has got it wrong (hey, it happens!)

Next up is the expansion of a feature called Journeys that Google launched earlier this year.

Journeys aims to help people retrace their steps online using all that data Google collects about users. By adding some ML wizardry, Google says Chrome will bring together all the pages you’ve visited around a specific topic. The idea is to put behind us the days of scrolling through our entire browser history to resume where we left off.

However, it’s the final feature that’s arguably the most interesting.

Google says that it will use ML to personalise Chrome’s toolbar in real-time based on the individual user.

“Maybe you like to read news articles in the morning – phone in one hand, cereal spoon in the other – so you share lots of links from Chrome. Or maybe voice search is more your thing, as you sneak in a few questions during your transit ride to work,” wrote Tarun Bansal, Chrome software engineer, in a blog post.

“Either way, we want to make sure Chrome is meeting you where you’re at, so in the near future, we’ll be using ML to adjust the toolbar in real-time – highlighting the action that’s most useful in that moment (e.g., share link, voice search, etc.)

Here’s how that will look:

The ML-powered features for Chrome really help to show how such models are improving our security while making day-to-day experiences more enjoyable.

(Image Credit: Google)

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Ian Goodfellow is a renowned machine learning researcher. Goodfellow invented generative adversarial networks (GANs), developed a system for Google Maps that transcribes addresses from Street View car photos, and more.

In a departure note to his team at Apple, Goodfellow cited the company’s much-criticised lack of flexibility in its work policies.

Many companies were forced into supporting remote work during the pandemic and many have since decided to keep flexible working due to the recruitment advantages, mental/physical health benefits, lowering the impact of rocketing fuel costs, improved productivity, and reduced office space costs.

Apple planned for employees to work from the office on Mondays, Tuesdays, and Thursdays, starting this month. However, following backlash, on Tuesday the company put the plan on hold—officially citing rising Covid cases.

Goodfellow already decided to hand in his resignation and head to a company with more forward-looking, modern working policies.

The machine learning researcher had worked for Apple since 2019. Prior to Apple, Goodfellow had previously worked for Google as a senior research scientist.

Goodfellow is now reportedly returning to Google, albeit to its DeepMind subsidiary. Google is currently approving requests from most employees seeking to work from home.

More departures are expected from Apple if it proceeds with its return-to-office mandate.

“Everything happened with us working from home all day, and now we have to go back to the office, sit in traffic for two hours, and hire people to take care of kids at home,” a different former Apple employee told Bloomberg.

Every talented AI researcher like Goodfellow that leaves Apple is a potential win for Google and other companies.

(Photo by Viktor Forgacs on Unsplash)

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The Cambridge-based firm was founded by mathematicians and cyber defense experts in 2013 and uses self-learning AI to protect enterprises across all industry sectors.

Machine learning is used to make thousands of “micro-level” decisions in the background as part of Darktrace’s autonomous response technology called Antigena.

Antigena has been improved with 70 new machine learning models to bolster its ability to autonomously neutralise attacks in real-time.

“The hallmark of a great AI solution is the ability to surpass automation to seamlessly blend into users’ everyday work rhythm,” said Jack Stockdale OBE, CTO of Darktrace.

“When developing Darktrace Cyber AI products, our goal is to augment and uplift the security team to make the task at hand more efficient, so the end product is very intuitive and helps users in their workflow journeys.”

Darktrace has made a conscious effort to adhere to XAI (Explainable AI) principles. XAI ensures that humans can access and understand the decisions taken by the AI.

The incident display for Cyber AI Analyst leverages natural language processing “to clearly outline the steps a human analyst would take if analyzing the same activity and highlights a concise incident summary outlining each stage, which is easy to understand and quick to triage.”

Furthermore, the incident display will also highlight all related events such as associated users, destination ports, and protocols used. The complete breakdown of the actions taken by Darktrace’s solution enables a human analyst to delve into any particular incident response.

Another key improvement in this release is to the Enterprise Immune System. Users can now use filters to narrow down incidents that have a particular severity or relate to specific classifications like compliance.

“With the latest release of Darktrace’s Enterprise Immune System, we really kept the user at the forefront of all UX/UI design decisions, from the beginning to the end of the AI product development life cycle,” explained Stockdale.

(Photo by Muhannad Ajjan on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

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The chip, the AML100, is the industry’s first analog tiny machine learning solution. In practice, that means always-on system power is reduced by 95 percent.

Key features:

• Consumes less than 20µA when always-sensing
• Intelligently reduces quantity of data by up to 100x while the data are still in analog
• Features field-programmable functionality to address a wide range of always-on applications
• Leverages patented analog compression technology for preroll collection to maintain accuracy of wake word engine in voice-enabled devices
• Supports 4 analog sensors in any combination (microphones, accelerometers, etc.)
• Available in 7mm x 7mm 48-pin QFN package

Devices that previously required a wired power connection – or large battery, where viable – can use the AML100 to create new product classes and/or enable more flexible deployments.

Tom Doyle, Founder and CEO of Aspinity, said:

“We’ve long realised that reducing the power of each individual chip within an always-on system provides only incremental improvements to battery life. That’s not good enough for manufacturers who need revolutionary power improvements.

The AML100 reduces always-on system power to under 100µA, and that unlocks the potential of thousands of new kinds of applications running on battery.”

Current always-on devices continuously collect vast amounts of natively analog data and therefore consume a large amount of power to process mostly irrelevant data.

Aspinity claims the AML100 moves the machine learning workload to ultra-low-power analog “where the AML100 can determine data relevancy with a high degree of accuracy and at near-zero power.”

The AML100 is set for mass production in Q4 2022.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

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From companies shifting their existing AI focus to adapting AI across their entire organisation, here are three predictions for 2022: 

Can all players in the AI space coexist?

Next year, organisations and their data scientists will see that the variety of tools that exist to automate AI and ML within their business cover an array of specialities, and that these tools can coexist depending on the user and their needs and priorities. Big AI companies would like to take it all, but the reality is that data ecosystems have become more specialised. 

This is why a more holistic approach is needed across institutions in order to break down silos and really understand what data they have. Allowing companies to have a more organised and streamlined approach for tasks and projects. This transition will be made successful through smaller, specialised AI companies, such as TurinTech, driven to develop new AI optimisation tools to help companies tackle their data challenges more efficiently. 

These tools will complement each other, enabling users to discover, interpret and communicate valuable insights through oceans of data, and build AI to support better decision-making faster and more seamlessly.

Companies will shift their focus to streamline AI 

As organisations realise the crucial benefits AI can facilitate for their business, 2022 will see priorities change for companies across the globe. The main goal of businesses will not just focus on incorporating AI into their operations, but also doing this as efficiently as possible. With AI assisting in cutting down company costs, increasing revenue, and gaining a competitive edge, organisations will prioritise scaling AI across their whole operations, not just in their data and technology departments. 

Businesses that have grown their data teams and capabilities in recent years will be looking to maximise the investments they have made in these teams. Companies will focus on streamlining their operations and building optimal AI efficiently at scale through collaborative platforms. By incorporating AI technology across their entire business, companies will improve efficiency, productivity and be able to pinpoint potential risks in order to avoid future setbacks and challenges. 

2022 will see a green future for AI

With sustainability being a hot topic around the globe as the world reflects on the recent COP26 conference, it’s time the tech industry rethinks the carbon footprint of AI too. Next year will see organisations rethinking their carbon footprint, also taking their technology and AI carbon footprint into consideration.

With green AI, businesses can receive accurate machine learning models which are faster, more productive, and consume less computing resources, all while increasing operating speed and reducing energy usage. Green AI ultimately integrates technology and sustainability into a unified ecosystem. 

When it comes to saving energy, AI can assist in developing precise predictive capabilities and intelligent grid systems to manage the demand and supply of renewable energy. By doing this, companies will be able to significantly decrease costs and unnecessary carbon pollution generation. 

Companies are using AI to help improve their sustainability goals and green initiatives. Alaska Airlines, for example, is successfully decreasing the company’s carbon footprint by using AI technology to guide flights, and more companies will follow suit next year. With an increase of businesses using green AI, sustainability initiatives can be reached and reduce their carbon footprint. 

The bottom line

2022 will be a big year for the AI industry, with businesses big and small realising the benefits of leveraging AI partners and smaller players specialising in niche fields, as well as actively adopting green AI in order to meet and maybe even exceed sustainability targets in the future.

(Photo by Ellen Melin on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

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PyTorch Live was unveiled during PyTorch Developer Day and enables anyone to build mobile ML demo apps using JavaScript, the world’s most popular programming language.

While on-device AI demos cannot currently be shared, Meta says that functionality is on the way. Developers can start building custom machine learning models to later share with the broader PyTorch community.

PyTorch was publicly launched by Meta back in January 2017, when the company was still known as Facebook. The open-source machine learning library quickly became a firm favourite among the developer and data science communities.

As the PyTorch name suggests, the main library’s interface is designed around Python but it also has a C++ interface. 

The once-dominant machine learning library, TensorFlow, had a two-year headstart on PyTorch but has been falling behind in usage in recent years.

In 2018, GitHub’s Octoverse report highlighted the growth of PyTorch as an open-source project outpacing that of TensorFlow. PyTorch grew by 2.8x that year compared to TensorFlow’s still not insubstantial 1.8x.

That edge for PyTorch appears to be eating into TensorFlow’s early mover advantage.

TensorFlow appeared in three times more job listings in Indeed, Monster, SimplyHired, and LinkedIn as PyTorch in April 2019. However, TensorFlow’s edge in job-listing mentions dropped to 2x in 2020.

Over the past year, PyTorch has also overtaken TensorFlow in worldwide Google searches:

PyTorch Live looks set to accelerate the success of the machine learning library. The tools use React Native for building cross-platform visual user interfaces and PyTorch Mobile powers on-device inference.

Anyone wanting to get started with PyTorch Live can do so through its command-line interface setup and/or its data processing API.

(Image Credit: Meta)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo North America on 11-12 May 2022.

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The image-cropping algorithm used by Twitter was intended to keep the most interesting parts of an image in the preview crop in people’s timelines. That’s all good, until users found last year that it favoured lighter skin colours over dark and the breasts and legs of women over their faces.

When researchers fed a picture of a black man and a white woman into the system, the algorithm displayed the white woman 64 percent of the time and the black man just 36 percent of the time. For images of a white woman and a black woman, the algorithm displayed the white woman 57 percent of the time.

Twitter has offered bounties ranging between $500 and $3500 to anyone who finds evidence of harmful bias in their algorithms. Anyone successful will also be invited to DEF CON, a major hacker convention.

Rumman Chowdhury, Director of Software Engineering at Twitter, and Jutta Williams, Product Manager, wrote in a blog post:

“We want to take this work a step further by inviting and incentivizing the community to help identify potential harms of this algorithm beyond what we identified ourselves.”

After initially denying the problem, it’s good to see Twitter taking responsibility and attempting to fix the issue. By doing so, the company says it wants to “set a precedent at Twitter, and in the industry, for proactive and collective identification of algorithmic harms.”

Three staffers from Twitter’s Machine Learning Ethics, Transparency, and Accountability department found biases in their own tests and claim the algorithm is, on average, around four percent more likely to display people with lighter skin compared to darker and eight percent more likely to display women compared to men.

However, the staffers found no evidence that certain parts of people’s bodies were more likely to be displayed than others.

“We found that no more than 3 out of 100 images per gender have the crop not on the head,” they explained in a paper that was published on arXiv.

Twitter has gradually ditched its problematic image-cropping algorithm and doesn’t seem to be in a rush to reinstate it anytime soon:

In its place, Twitter has been rolling out the ability for users to control how their images are cropped.

“We considered the trade-offs between the speed and consistency of automated cropping with the potential risks we saw in this research,” wrote Chowdhury in a blog post in May.

“One of our conclusions is that not everything on Twitter is a good candidate for an algorithm, and in this case, how to crop an image is a decision best made by people.”

The HackerOne page for the challenge can be found here.

(Photo by Edgar MORAN on Unsplash)

Find out more about Digital Transformation Week North America, taking place on November 9-10 2021, a virtual event and conference exploring advanced DTX strategies for a ‘digital everything’ world.

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Most current AR applications are somewhat gimmicky, with barely a handful that have achieved any form of mass adoption. Apple’s decision to introduce LiDAR in its recent devices has given AR a boost but it’s clear that more needs to be done to make applications more useful.

A newly filed patent suggests that Apple is exploring how machine learning can be used to automatically (or “automagically,” the company would probably say) detect objects in AR.

The first proposed use of the technology would be for Apple’s own Measure app.

Measure’s previously dubious accuracy improved greatly after Apple introduced LiDAR but most people probably just grabbed an actual tape measure unless they were truly stuck without one available.

The patent suggests machine learning could be used for object recognition in Measure to help users simply point their devices at an object and have its measurements automatically presented in AR.

Specifically, Apple’s patent suggests displaying a “measurement of the object determined using one of a plurality of class-specific neural networks selected based on the classifying of the object.”

This simplicity benefit over a traditional tape measure would likely drive greater adoption.

Machine learning is already used for a number of object recognition and labelling tasks within Apple’s ecosystem. Image editor Pixelmator Pro, for example, uses it to automatically label layers.

Apple’s implementation suggests an object is measured “by first generating a 3D bounding box for the object based on the depth data”. This boundary box is then refined “using various neural networks and refining algorithms described herein.”

Not all objects are measured the same so Apple suggests that a neural network could also step in here to determine what could be useful for the user. For example, “a seat height for chairs, a display diameter for TVs, a table diameter for round tables, a table length for rectangular tables, and the like.”

To accomplish what Apple envisions here, a lot of models will need to be trained for all objects. However, there are many of the more everyday items that could be supported early on—with more added over time.

“One model may be trained and used to determine measurements for chair type objects (e.g., determining a seat height, arm length, etc.),” Apple wrote, “and another model may be trained and used to determine measurements for TV type objects (e.g., determining a diagonal screen size, greatest TV depth, etc.)”

Five inventors are credited with the patent: Amit Jain, Aditya Sankar; Qi Shan, Alexandre Da Veiga, and Shreyas V Joshi.

Apple’s patent is another example of how machine learning can be combined with other technologies to add real utility and ultimately improve lives. There’s no telling when, or even if, Apple will release an updated Measure app based on this patent—but it seems more plausible in the not-so-distant future than many of the company’s patents.

(Image Credit: Apple)

Find out more about Digital Transformation Week North America, taking place on November 9-10 2021, a virtual event and conference exploring advanced DTX strategies for a ‘digital everything’ world.

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Vertex was announced during this year’s virtual I/O developer conference and somewhat breaks from Google’s tradition of using its keynote to focus more on updates to its mobile and web development solutions. Google announcing the platform during the keynote shows how important the company believes it to be for a wide range of developers.

Google claims that using Vertex enables models to be trained with up to 80 percent fewer lines of code when compared to competing platforms.

Bradley Shimmin, Chief Analyst for AI Platforms, Analytics, and Data Management at Omdia, said:

“Data science practitioners hoping to put AI to work across the enterprise aren’t looking to wrangle tooling. Rather, they want tooling that can tame the ML lifecycle. Unfortunately, that is no small order.

It takes a supportive infrastructure capable of unifying the user experience, plying AI itself as a supportive guide, and putting data at the very heart of the process — all while encouraging the flexible adoption of diverse technologies.”

Vertex brings together Google Cloud’s AI solutions into a single environment where models can go from experimentation all the way to production.

Andrew Moore, VP and GM of Cloud AI and Industry Solutions at Google Cloud, said:

“We had two guiding lights while building Vertex AI: get data scientists and engineers out of the orchestration weeds, and create an industry-wide shift that would make everyone get serious about moving AI out of pilot purgatory and into full-scale production.

We are very proud of what we came up with in this platform, as it enables serious deployments for a new generation of AI that will empower data scientists and engineers to do fulfilling and creative work.”

Vertex provides access to Google’s MLOps toolkit which the company uses internally for workloads involving computer vision, conversation, and language.

Other MLOps features supported by Vertex include Vizier, which increases the rate of experimentation; Feature Store to help practitioners serve, share, and reuse ML features; and Experiments to accelerate the deployment of models into production with faster model selection.

Some high-profile companies were given early access to Vertex. Among them is ModiFace, a part of L’Oréal that focuses on the use of AR and AI to revolutionise the beauty industry.

Jeff Houghton, COO at ModiFace, said:

“We provide an immersive and personalized experience for people to purchase with confidence whether it’s a virtual try-on at web check out, or helping to understand what brand product is right for each individual.

With more and more of our users looking for information at home, on their phone, or at any other touchpoint, Vertex AI allowed us to create technology that is incredibly close to actually trying the product in real life.”

ModiFace uses Vertex to train AI models for all of its new services. For example, the company’s skin diagnostic service is trained on thousands of images from L’Oréal’s Research & Innovation arm and is combined with ModiFace’s AI algorithm to create tailor-made skincare routines.

Another firm that is benefiting from Vertex’s capabilities is Essence, a media agency that is part of London-based global advertising and communications giant WPP.

With Vertex AI, Essence’s developers and data analysts are able to regularly update models to keep pace with the rapidly-changing world of human behaviours and channel content.

Those are just two examples of companies whose operations are already being greatly enhanced through the use of Vertex. Now the floodgates have been opened, we’re sure there’ll be many more stories over the coming years and we can’t wait to hear about them.

You can learn how to get started with Vertex AI here.

(Photo by John Baker on Unsplash)

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Machine learning uses powerful algorithms to discover insights based on real-world data that can then be used to make predictions about future outcomes. As new data comes available, machine learning programs can automatically adapt and produce updated predictions. As with any tool, machine learning is not a silver bullet. However, there are many situations in which the technology can outperform linear and statistical algorithms.

Here are five of the most common use cases where machine learning can make a big difference:

When engineers can’t code rules for certain problems

Many human-oriented tasks (such as recognising whether an email is spam) aren’t solvable using simple (deterministic), rule-based solutions. Because so many factors may influence an answer, engineers would have to write and frequently update billions of lines of code. In addition, when rules depend on too many factors, and when those rules overlap or need fine-tuning, it becomes difficult for humans to code precise rules. Fortunately, machine learning programs don’t require users to encode actual patterns. These programs only need proper algorithms to extract patterns automatically.

When you need to scale a solution to millions of cases

You might be able to manually categorise a few hundred payments as either fraudulent or not. However, this becomes tedious or impossible when dealing with millions of transactions. As user bases grow, it’s no longer feasible for organisations to process payments by hand – end-users today want answers about their money in milliseconds, not minutes or hours. Machine learning solutions are effective at handling these types of large-scale problems with little or no human intervention.

When you can do it manually, but it’s not cost-efficient

There are situations in which in-house experts could process many requests quickly and accurately but at a high cost. For instance, imagine you assess DMV forms for in-state and cross-state car purchases to determine their validity before passing them on. In this situation, the business processes are well-defined, optimised, and serialised. It may take only a few minutes to check each form thoroughly. But allocating so much manual labor to this work is likely not the best use for your budget. Machine learning, on the other hand, offers predictable, pay-as-you-go pricing for fully scaled operations.

When you have a massive dataset without obvious patterns

Consider this – you’ve successfully prepared a well-curated dataset and know the underlying problem. However, you don’t see any explicit patterns in the data, preventing you from encoding those validations. Plus, there are many typos, missing fields, and other human-caused errors with no validation in place. You may even know the data is poor quality and can manually determine every affected row. But you can’t see any actual connections between valid and invalid records. Machine Learning algorithms can solve this problem. They can find hidden connections between data points that aren’t clear to humans. Tools like Interpreting Tracers can even describe how machine learning models arrive at their conclusion.

When you live in an ever-changing universe (adaptive)

The world, and its problems, are always changing. A problem you solved yesterday can easily mutate into something else entirely, rendering your previous solution inefficient or even useless. For example, if your organisation processed medical appointment recordings to extract diagnoses, procedure information, and billing codes, your rules might have to evolve constantly. However, you can’t make updates in real-time 24/7. Meanwhile, incorrectly labelled items could lead to insurance rejections, huge fines, and legal penalties. One major advantage of machine learning methods is that they can learn from data across the entire lifecycle of your application – from the first line of code written to the moment when the model is finally shut down. Moreover, it’s important for production-grade systems to have feedback loops so that you can catch the moment when your model no longer solves problems correctly.

It’s important to remember that machine learning is a tool – it’s not magic. Machine learning models are essentially advanced math-based algorithms, which identify patterns in data and learn from them. However, when properly applied to the right use cases, machine learning can reduce the amount of time spent error-prone manual IT operations, adding significant business value and greatly reducing IT costs.

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

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