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Respected journalist Steven Levy has scored another nice exclusive with a new write-up over at Backchannel, a Wired Media Group property, giving us a rare inside look at how artificial intelligence and machine learning work at Apple.

Welcome to The Temple of Machine Learning

Apple has “a lot” of engineers working on machine learning—some of which weren’t necessarily trained in the field before they joined Apple—and any results of their work are shared and readily available to other product teams within the company.

“We don’t have a single centralized organization that’s the Temple of Machine Learning in Apple,” says Apple’s boss of Software Engineering, Craig Federighi. “We try to keep it close to teams that need to apply it to deliver the right user experience.”

Of course some talent comes from acquisitions as Apple’s recently been buying 20 to 30 small companies a year. “We’re looking at people who have that talent but are really focused on delivering great experiences,” said Federighi.

Making Siri sound more like an actual person

Before we get to it, there are four aspects to Siri:

Speech recognition—turning speech to text

Natural language understanding—understanding what you’re saying

Execution—fulfilling a query or request

Response—talking back to you

Although Apple was still licensing much of its speech technology for Siri from a third party (likely Nuance), that’s “due for a change,” Levy writes, as Apple explores in-house speech capabilities for the digital personal assistant.

In fact, it’s deep learning that enables Siri to talk back to you more naturally while machine learning techniques smooth out recordings of individual words, making Siri responses sound more like an actual person than when it relied on Nuance.

All of that also helps with bringing Siri to your favorite apps in a way that doesn’t require you to use specific language to access third-party skills on iOS 10 and macOS Sierra.

Siri’s brain transplant

On July 30, 2014, Siri had a brain transplant.

That’s when Apple began moving Siri’s voice recognition to a neural-net based system following initial complaints from users regarding erroneously interpreted commands. They next trained the neural net for Siri using lots of data and GPUs.

“We have the biggest and baddest GPU (graphics processing unit microprocessor) farm cranking all the time,” says Siri’s Alex Acero. “And we pump lots of data.” According to Acero, Siri began using machine learning to understand user intent in November 2014, and released a version with deeper learning a year later.

By applying machine learning techniques such as deep neural networks (DNN), convolutional neural networks, long short-term memory units, gated recurrent units and n-grams, Apple was able to infuse Siri with deep learning and cut her error rate by a factor of two in all the languages.

“That’s mostly due to deep learning and the way we have optimized it — not just the algorithm itself but in the context of the whole end-to-end product,” says Acero.

The iBrain is here and with iOS 10, it’s already in your phone thanks to machine learning, artificial intelligence, the power of Apple-designed silicon and Differential Privacy techniques.

The size of Apple’s user base allowed Apple to quickly train Siri.

Steve Jobs said you’re going to go overnight from a pilot, an app, to a hundred million users without a beta program. All of a sudden you’re going to have users. They tell you how people say things that are relevant to your app. That was the first revolution. And then the neural networks came along.

But where else does machine learning help Apple?

Apple and machine learning

Apple uses deep learning to detect fraud on the Apple store, to extend battery life between charges on all your devices, and to help it identify the most useful feedback from thousands of reports from its beta testers. Machine learning helps Apple choose news stories for you. It determines whether Apple Watch users are exercising or simply perambulating.

It recognizes faces and locations in your photos. It figures out whether you would be better off leaving a weak Wi-Fi signal and switching to the cell network. It even knows what good filmmaking is, enabling Apple to quickly compile your snapshots and videos into a mini-movie at a touch of a button.

If you were to argue that Apple’s competitors do many similar things, you’d be right.

Clockwise, from upper left: Tom Gruber, Siri’s Advanced Development Head; Eddy Cue, Apple’s SVP of Internet Software and Services; and Alex Acero, Siri’s Senior Director.

But, you must keep in mind that Apple’s way of providing these features also protects your privacy. In using the power of in-house designed A-series chips that power iOS devices and Differential Privacy techniques, Apple’s software is able to pull those things off directly on the device, without dumping significant user data on its servers to let the cloud do the heavy-lifting.

RELATED: A closer look at Differential Privacy on iOS 10 and macOS Sierra

In a rare revelation, Apple told the journalist that the dynamic cache which enables machine learning on the iPhone takes up about 200 megabytes of storage space.

The system deletes older data, but the actual size of the cache depends on how much personal data is used, stuff like information about app usage, interactions with other people, neural net processing, a speech modeler, natural language event modeling and data for the neural nets that power object recognition, face recognition and scene classification in the Photos app.

Here’s Apple’s marketing boss Phil Schiller:

Our devices are getting so much smarter at a quicker rate, especially with our Apple-designed A- series chips. The back ends are getting so much smarter, faster, and everything we do finds some reason to be connected. This enables more and more machine learning techniques, because there is so much stuff to learn, and it’s available to [us].

These techniques are also used to power some “new things we haven’t be able to do,” he added.

But it’s not just the silicon, says Federighi:

Apple isn’t just dipping its toes in Differential Privacy, it’s fully embracing it.

“It’s a technique that will ultimately be a very Apple way of doing things as it evolves inside Apple and in the ways we make products,” said Schiller.

Less visible AI-driven features

Artificial intelligence is also used to improve other aspects of the iOS experience.

Here are some of the examples of AI-driven features:

Identifying who a caller not in your Contacts, based on their emails

Populating the iOS app switcher with a shortlist of the apps you’re most likely to open next

Getting reminders from appointments based on emails

Surfacing a Maps location for the hotel you’ve reserved before you type it in

Remembering where you parked your car

Palm rejection for Apple Pencil

Recognizing ‘Hey Siri’ hot word in a power-friendly way

Improving QuickType keyboard suggestions

And, of course, lots more.

Software boss Craig Federighi listens to Siri’s speech guru Alex Acero discussing the voice recognition software at Apple headquarters. Oh, how I’d like to be a fly on that wall!

“Machine learning is enabling us to say yes to some things that in past years we would have said no to,” says marketing honcho Phil Schiller. “It’s becoming embedded in the process of deciding the products we’re going to do next,” says Schiller, adding:

The typical customer is going to experience deep learning on a day-to-day level that [exemplifies] what you love about an Apple product. The most exciting [instances] are so subtle that you don’t even think about it until the the third time you see it, and then you stop and say, “How is this happening?”

Thanks to the article, I also learned that some of the AI code that recognizes hand-scrawled Chinese characters into text on iOS or power the new letter-by-letter Apple Watch input on watchOS 3 actually draws from Apple’s work on handwriting recognition for its failed Newton message pad in the early 1990s.

The piece provides additional insights into how Apple approaches artificial intelligence and machine learning work versus its competitors. It’s a fascinating article and you’ll wholeheartedly recommended to give it a read.

Source: Backchannel

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Artificial Intelligence Creates Synthetic Data For Machine Learning

Introduction Artificial Intelligence Creates Synthetic Data for Machine Learning

Generative Adversarial Networks are one of the main tools that artificial intelligence is used to produce synthetic data (GANs). A generator plus a Bayesian classifier make up a GAN, a particular kind of neural network. The generator oversees producing fake data, while the discriminator determines if the data is real or fake. Together, the two networks are trained, with the generator attempting to produce data that the discriminator finds difficult to separate from actual data and the discriminator working to become more adept at recognizing artificial information.

Synthetic Data

Two sources exist for synthetic data −

Real World Data

Simulated Data

Although personally identifying information (PII) and personal health information (PHI) can be removed from real-world data, this does not completely protect privacy since the data records can still be matched to other sources that can be used to identify individuals. Like the COVID-19 example, the anonymized data must be mixed again in a way that keeps all the data set’s statistical characteristics for the machine learning algorithms to make accurate inferences and develop accurate rules.

In some cases, a lack of real-world data is a challenge for machine learning. Sometimes it would be impractical or too expensive to acquire data from the real world. Simulated data may occasionally be close enough to real-world instances for machine learning algorithms to recognise it. The self-driving car industry, for instance, blends real sensor data from moving vehicles with simulated data from driving simulations (even video games like Grand Theft Auto).

Unfortunately, adopting synthetic data comes with several difficulties that must be overcome. The requirement that the synthetic data be representational of the real-world data presents one of the key obstacles. The machine learning model may not function well if the synthetic data does not precisely match the real-world data. Another challenge is that the synthetic data must be sufficiently varied to account for every scenario that the model might face in the actual world.

Another challenge is that biassed models might be produced using fake data. Biased models are models that have learned to produce inaccurate predictions for certain groups of people. For example, a model that is trained on synthetic data that is biased towards a particular race or gender may produce inaccurate predictions for people who are not in that group. To avoid this, it is important to ensure that the synthetic data is diverse and representative of the real-world data.

Synthetic Data Applications

Software Testing that is automated for DevOps. Test data has always been necessary for software development, but today’s quick Agile development cycles of DevOps demand more test data than ever.

Robots and Automation in manufacturing. Synthetic data can speed up the training of AI systems in robotics and manufacturing applications because real-world data collecting can be sluggish and expensive, like automobile data collection.

Monetary services. Personal financial data is subject to strict confidentiality restrictions, just like healthcare data, and synthetic data provides developers and business users with access to larger datasets without invading privacy.

Consumer Behavior Simulations in Marketing. Since the GDPR and other restrictions apply to actual consumer online behavior, marketing AI can be trained more broadly and thoroughly using a synthetic dataset.

Clinical Medical Investigation. Since PHI is heavily regulated, artificial intelligence (AI) and machine learning are made viable in situations where datasets might otherwise be too limited to be helpful.

Facial Identification to avoid privacy violations and biases from underrepresented types of faces, synthetic facial data can be used instead of real-world pictures to train facial recognition.

Conclusion

In conclusion, AI is being used to create synthetic data that can be used to train machine learning models. Synthetic data can be used to augment limited real-world data sets, as well as to create data for tasks that are difficult or impossible to collect real-world data for. However, it is important to ensure that the synthetic data is representative of the real-world

Artificial Intelligence (Ai) And Deep Learning

The horizon of what repetitive tasks a computer can replace continues to expand due to artificial intelligence (AI) and the sub-field of deep learning (DL).

Artificial intelligence gives a device some form of human-like intelligence.

Researchers continue to develop self-teaching algorithms that enable deep learning AI applications like chatbots.

To understand deep learning better, we need to understand it as part of the AI evolution:

See more: Artificial Intelligence Market

Partly to eliminate human-based shortcomings in machine learning, researchers continue to try to create smarter ML algorithms. They design neural networks within ML that can learn on their own from raw, uncategorized data. Neural networks — the key to deep learning — incorporate algorithms based on mathematical formulas that add up weighted variables to generate a decision.

One example of a neural network algorithm is all of the possible variables a self-driving car considers when making the decision if it should proceed forward: is something in the way, is it dangerous to the car, is it dangerous to the passenger, etc. The weighting prioritizes the importance of the variables, such as placing passenger safety over car safety.

Deep learning extends ML algorithms to multiple layers of neural networks to make a decision tree of many layers of linked variables and related decisions. In the self-driving car example, moving forward would then lead to decisions regarding speed, the need to navigate obstacles, navigating to the destination, etc. Yet, those subsequent decisions may create feedback that forces the AI to reconsider earlier decisions and change them. Deep learning seeks to mimic the human brain in how we can learn by being taught and through multiple layers of near-simultaneous decision making.

Deep learning promises to uncover information and patterns hidden from the human brain from within the sea of computer data.

AI with deep learning surrounds us. Apple’s Siri and Amazon’s Alexa try to interpret our speech and act as our personal assistants. Amazon and Netflix use AI to predict the next product, movie, or TV show we may want to enjoy. Many of the websites we visit for banking, health care, and e-commerce use AI chatbots to handle the initial stages of customer service.

Deep learning algorithms have been applied to:

Customer service: Conversational AI incorporates natural language processing (NLP), call-center style decision trees, and other resources to provide the first level of customer service as chatbots and voicemail decision trees.

Conversational AI incorporates, call-center style decision trees, and other resources to provide the first level of customer service as chatbots and voicemail decision trees.

Cybersecurity: AI analyzes log files, network information, and more to detect, report, and remediate malware and human attacks on IT systems.

Financial services: Predictive analytics trade stocks, approve loans, flag potential fraud, and manage portfolios.

Health care: Image-recognition AI reviews medical imaging to aid in medical analysis

Law enforcement:

Track payments and other financial transactions for signs of fraud, money laundering, and other crimes

Extract patterns from voice, video, email and other evidence

Analyze large amounts of data quickly

See more: Artificial Intelligence: Current and Future Trends

We do not currently have AI capable of thinking at the human level, but technologists continue to push the envelope of what AI can do. Algorithms for self-driving cars and medical diagnosis continue to be developed and refined.

So far, AI’s main challenges stem from unpredictability and bad training data:

Biased AI judge (2023)

: To the great dismay of those trying to promote AI as unbiased, an AI algorithm designed to estimate recidivism, a key factor in sentencing, produced biased sentencing recommendations. Unfortunately, the AI learned from historical data which has racial and economic biases baked into the data; therefore, it continued to incorporate similar biases.

AI consists of three general categories: artificial narrow intelligence (ANI) focuses on the completion of a specific task, such as playing chess or painting a car on an assembly line; artificial general intelligence (AGI) strives to reach a human’s level of intelligence; and artificial super intelligence (ASI) attempts to surpass humans. Neither of these last two categories exists, so all functional AI remains categorized as ANI.

Deep learning continues to improve and deliver some results, but it cannot currently reach the higher sophistication levels needed to escape the artificial narrow intelligence category. As developers continue to add layers to the algorithms, AI will continue to assist with increasingly complex tasks and expand its utility. Even if human-like and superhuman intelligence through AI may be eluding us, deep learning continues to illustrate the increasing power of AI.

See more: Top Performing Artificial Intelligence Companies

A Look At Ai In Cfd Trading

Predictive Analysis

If traders could have one wish, it would be to be able to predict how the market is going to move accurately. The vast analytic potential of Artificial Intelligence makes that fantasy come one step closer.

Demand for Tech

CFD and other areas of financial trading aren’t dominated by the older generation. It’s an industry which has a fresh flow of younger participants, and that means there is a constant demand for cutting edge tech. With the move away from desk-based trading and the switch to using mobile apps, Artificial Intelligence is proving to be essential to keep up to date while on the move. More efficient and intuitive software means that it’s now possible to get the latest information at a glance rather than poring over manual reports. When you’re short on time but want to stay ahead on your trades, this type of access is critical.

Are Trading Bots the Future?

Artificial Intelligence (AI) is a concept which has pervaded every area of business, offering new opportunities which were hitherto impossible. This is especially the case within financial trading in areas such as CFDs where speed and reduction of errors are absolutely vital. Of course, you can’t merely slot AI in and expect it to do all the work. You’ll still need to have a fundamental working knowledge of CFDs yourself. You can find detailed guides on CFD trading which will provide an excellent foundation for learning. Once you’re up to speed, it’s possible to enhance your knowledge with AI-backed software. Here’s what you need to know about the chúng tôi traders could have one wish, it would be to be able to predict how the market is going to move accurately. The vast analytic potential of Artificial Intelligence makes that fantasy come one step closer. Analytics are an essential part of many businesses , but in trading, their function is vital. AI has the edge over human calculations for its ability to draw comprehensively on historical algorithms and current data to create market analysis which is far more likely to be accurate. While it’s impossible to predict the future with any absolute guarantee, AI can use massive data sets to improve the statistical model, creating an outcome with an improved probability. All of this can be done very rapidly, outperforming previous analysis on both speed and accuracy. Access to AI trading software could therefore vastly alter your predictions, delivering more profitable trades time and chúng tôi and other areas of financial trading aren’t dominated by the older generation. It’s an industry which has a fresh flow of younger participants, and that means there is a constant demand for cutting edge tech. With the move away from desk-based trading and the switch to using mobile apps, Artificial Intelligence is proving to be essential to keep up to date while on the move. More efficient and intuitive software means that it’s now possible to get the latest information at a glance rather than poring over manual reports. When you’re short on time but want to stay ahead on your trades, this type of access is chúng tôi financial sector has historically led the way in Artificial Intelligence and continues to do with the exploration of new ways to utilise technology to its fullest. One possible option is the use of AI-guided bots, allowing CFD trades to be carried out automatically. As a rule-based system, AI isn’t as flexible as human traders, but where markets move rapidly, they offer a potential way of executing trades for maximum profit. As machine learning continues to improve, bots could prove to be a significant asset in trading. The idea may sound daunting but to keep up with competitors, it’s going to be essential to embrace the concept of Artificial Intelligence. It’s a type of technology that’s here to stay and could be the stepping stone that many traders have been waiting for.

A Look At Crypto And How It Can Benefit From Big Data Analytics

Since 2009, the popularity of the cryptocurrency industry has increased rapidly, establishing a new type of digital investing.

The Rise of Crypto

As many people know, cryptocurrencies are digital payment forms that people can use to buy online goods and services. The technology behind the crypto market is blockchain, a digital ledger that records transactions and validates them through proof of work or proof of stake. Cryptocurrencies are found in many industries today, including retail and online gambling. For example, in countries like Canada, you will find digital currencies such as Bitcoin accepted at retail stores like Birks Group, and there are many

Crypto and Big Data Analytics

Despite so many industries capitalizing on the popularity of cryptocurrencies, the market still has its problems. Fortunately, big data analytics can solve many of these issues, such as by helping crypto users better identify the future of the market and the value of the thousands of digital currencies available to purchase and trade. That is because data analytics tools can

Since 2009, the popularity of the cryptocurrency industry has increased rapidly, establishing a new type of digital investing. Reports now suggest more than 100 million people hold virtual currencies, and there are thousands of transactions per day for cryptocurrencies like Bitcoin and Ethereum. However, as cryptocurrencies continue to grow, so too does market volatility and the amount of transactional data. Because of this, it can be hard to maximize profits and identify future trends, which is why the crypto market can benefit from adopting techniques from big data chúng tôi many people know, cryptocurrencies are digital payment forms that people can use to buy online goods and services. The technology behind the crypto market is blockchain, a digital ledger that records transactions and validates them through proof of work or proof of stake. Cryptocurrencies are found in many industries today, including retail and online gambling. For example, in countries like Canada, you will find digital currencies such as Bitcoin accepted at retail stores like Birks Group, and there are many crypto casinos in Canada available in the North American country. These casinos have many benefits, such as providing crypto users (Bitcoin, Litecoin, etc.) with fast transaction speeds, higher bonuses, and the lowest possible minimum deposit and withdrawal limits. Even corporate giants like Microsoft, AT&T, and Starbucks have started accepting cryptocurrencies in Canada.Despite so many industries capitalizing on the popularity of cryptocurrencies, the market still has its problems. Fortunately, big data analytics can solve many of these issues, such as by helping crypto users better identify the future of the market and the value of the thousands of digital currencies available to purchase and trade. That is because data analytics tools can help uncover trends by looking at historical data, which crypto investors can use to make informed decisions about where the crypto market is going. Let’s say someone uses a crypto dashboard to monitor their coins and transactions. Dashboards are one of the best data visualization tools in the analytics market because they help bring visibility to your investments and finances. When someone connects their crypto wallet to a dashboard, they are able to track their performance in real-time and look at how much they have invested in different cryptocurrencies. Crypto dashboards also allow people to study ROI, acquisition cost vs. profit/loss, and the exchange rate of their cryptocurrencies. Examining a particular cryptocurrency’s exchange rate history will inform you of how stable the coin is compared to others.

Source: Pixabay

A Deeper Look At Google Signals Map And Social Data Hub

If you’re anything like me, you stumbled across a Google Signals Map and had no idea it even existed. However, Google Signals has actually been available for quite some time, reported first by MarketingLand and also recently featured on Social Media Today, and more and more companies are beginning to explore this feature and what it can do for their SEO. After all, when Google comes out with a tool we all know there must be some sort of value. With all of the information at their fingertips, Google Signals doesn’t disappoint.

How Google Signals Map Works

It’s first important to understand where you can find the Signals Map and how it was created in the first place. Believe it or not, it was in 2009 that Google created their PuSH (PubSubHubbub) protocol. This was designed as a way for different social networks to send their activity data to Google in order to help companies keep everything in one place. It wasn’t until January 2012 that Google took things to the next level.

In 2012, Google took this PuSH protocol and integrated all of this data with Google Analytics. This is where the Social Reports section of Google Analytics arose, and while not all social networks are on board just yet (more about that later), Google has enough data for companies to really begin seeing the big picture when it comes to social media. Google then created a map of this data to help companies even further:

As you can see from the photo above, Google looks at five different categories as the most important when it comes to analyzing your social “ecosystem” as they say. If a social network hasn’t taken the steps to feed its data into the protocol, then that information will be indexed as a post.

How Social Networks Can Get Involved

It is up to the social networks to integrate their stream of data into the social data hub. There are a few different benefits that Google outlines as to why social networks can benefit from integrating:

Visibility. Your social network will be visible to any businesses and publishers using Google Analytics. Google gives you more attention, and so it makes sense that these networks will get more visibility than those that haven’t integrated.

Cost. It’s completely free for a social network to sign up and get involved, and it doesn’t take much time. You can learn more here about how to get started.

Measurement. Being a part of the social data hub means that your social network is a part of promoting the global social media landscape by providing accessible measurement of all platforms involved.

At this point, there are over 30 integrated into the social data hub. You will notice networks like Facebook and Twitter missing from the list, but this doesn’t mean that there is not valuable data to be gathered from those that are involved. Some of the most popular networks include:

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