ARTH-TASK-5 ML Industry Use-Case

First of All What is MACHINE LEARNING?

Machine learning is the subset of artificial intelligence that involves the study and use of algorithms and statistical models for computer systems to perform specific tasks without human interaction. Machine learning models rely on patterns and inference instead of manual human instruction. Most any task that can be completed with a data-defined pattern or set of rules can be done with machine learning. This allows companies to automate processes that were previously only possible for humans to perform—think responding to customer service calls, bookkeeping, and reviewing resumes. 

To extract machine learning value, a model must be trained to react to certain data in certain ways, which requires a lot of clean training data. Once the model successfully works through the training data and is able to understand the nuances of the patterns its learning, it will be able to perform the task on real data. We’ll walk through some use cases of machine learning to help you understand the value of this technology.

Types of machine learning

Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. The type of algorithm a data scientist chooses to use depends on what type of data they want to predict.

• Supervised learning. In this type of machine learning, data scientists supply algorithms with labeled training data and define the variables they want the algorithm to assess for correlations. Both the input and the output of the algorithm is specified.
• Unsupervised learning. This type of machine learning involves algorithms that train on unlabeled data. The algorithm scans through data sets looking for any meaningful connection. Both the data algorithms train on and the predictions or recommendations they output are predetermined.
• Semi-supervised learning. This approach to machine learning involves a mix of the two preceding types. Data scientists may feed an algorithm mostly labeled training data, but the model is free to explore the data on its own and develop its own understanding of the data set.
• Reinforcement learning. Reinforcement learning is typically used to teach a machine to complete a multi-step process for which there are clearly defined rules. Data scientists program an algorithm to complete a task and give it positive or negative cues as it works out how to complete a task. But for the most part, the algorithm decides on its own what steps to take along the way.

What is machine learning used for?

Machine learning has many potential uses, including external (client-facing) applications like customer service, product recommendation, and pricing forecasts, but it is also being used internally to help speed up processes or improve products that were previously manual and time-consuming. You’ll notice these two types throughout our list of machine learning use cases below.

Voice assistants.

• This consumer-based use for machine learning applies mostly to smart phones and smart home devices. The voice assistants on these devices use machine learning to understand what you say and craft a response. The machine learning models behind voice assistants were trained on human languages and variations in the human voice, because it has to translate what it hears into words and then make an intelligent, on-topic response. 
• Dynamic pricing. This machine–based pricing strategy is most known in the travel industry. Flights, hotels, and other travel bookings usually have a dynamic pricing strategy behind them. Consumers know that the sooner they book their trip the better, but they may not realize that the actual price changes are made via machine learning. 
• Email filtering. This is a classic use of machine learning. Email inboxes also have a spam inbox, where your email provider automatically filters unwanted spam emails. But how do they know when an email is spam? They have trained a model to identify spam emails based on characteristics they have in common. This includes the content of the email itself, the subject, and the sender. If you’ve ever looked at your spam inbox, you know that it wouldn’t be very hard to pick out spam emails because they look very different from real emails.
• Product recommendations. Amazon and other online retailers often list “recommended products” for each consumer individually. These recommendations are based on past purchases, browsing history, and any other behavioral information they have about consumers. Often the recommendations are helpful in finding related items that you need to complement your purchase (think batteries for a new electronic gadget). 
• Personalized marketing. Marketing is becoming more personal as technologies like machine learning gain more ground in the enterprise. Now that much of marketing is online, marketers can use characteristic and behavioral data to segment the market. Digital ad platforms allow marketers to choose characteristics of the audience they want to market to, but many of these platforms take it a step further and continuously optimize the audience based on who clicks and/or converts on the ads. The marketer may have listed 4 attributes they want their audience to have, but the platform may find 5 other attributes that make users more likely to respond to the ads. 
• Process automation. There are many processes in the enterprise that are much more efficient when done using machine learning. These include analyses such as risk assessments, demand forecasting, customer churn prediction, and others. These processes require a lot of time (possibly months) to do manually, but the insights gained are crucial for business intelligence. But if it takes months to get insights from the data, the insights may already be outdated by the time they are acted upon. Machine learning for process automation alleviates the timeliness issue for enterprises. 

Fraud detection Banks use machine learning for fraud detection to keep their consumers safe, but this can also be valuable to companies that handle credit card transactions. Fraud detection can save money on disputes and chargebacks, and machine learning models can be trained to flag transactions that appear fraudulent based on certain characteristics. 

Popular Machine Learning Applications and Use Cases in our Daily Life

1. Machine Learning Use Cases in Smartphones

Did you know that machine learning powers most of the features on your smartphone?

That’s right! From the voice assistant that sets your alarm and find you the best restaurants to the simple use case of unlocking your phone via facial recognition – machine learning is truly embedded in our favorite devices.

Voice Assistants

That example we saw in the introduction about talking to our virtual assistant? That was all about the concept of speech recognition – a budding topic in machine learning right now.

Voice assistants are ubiquitous right now. You must have used (or at least heard about) the below popular voice assistants:

• Apple’s Siri
• Google Assistant
• Amazon’s Alexa
• Google Duplex
• Microsoft’s Cortana
• Samsung’s Bixby

Smartphone Cameras

Wait – what in the world does machine learning have to do with my smartphone camera? Quite a lot, as it turns out.

The incredible images we are able to click these days and the depth of these images – all of that is thanks to machine learning algorithms. They analyze every pixel in a given image to detect objects, blur the background, and a whole host of tricks.

These machine learning algorithms do several things to improve and enhance the smartphone’s camera:

• Object detection to locate and single out the object(s) (or human) in the image
• Filling in the missing parts in a picture
• Using a certain type of neural network using GANs to enhance the image or even extend its boundaries by imagining what the image would look like, etc.

App Store and Play Store Recommendations

I love this feature of both Google’s Play Store and Apple’s App Store. The ‘Recommended for you’ section is based on the applications I have installed on my phone (or previously used).

For example, if I have a few sports and food-related applications – so my recommended for you section is usually filled with applications that are similar to these apps. I appreciate that the Play Store is personalized to my taste and shows me apps I have a higher chance of downloading.

Face Unlock – Smartphones

Most of us have are quite familiar with this. We pick up our smartphone and it unlocks itself by detecting our face. It’s smart, efficient, time-saving and frankly superb.

What a lot of people don’t know about this is that our smartphones use a technique called facial recognition to do this. And the core idea behind facial recognition is powered by – you guessed it – machine learning.

The applications of facial recognition are vast and businesses around the world are already reaping the benefits:

• Facebook uses it to identify the people in images
• Governments are using it to identify and catch criminals
• Airports are using to verify passengers and crew members, and so on

The usage of face recognition models is only going to increase in the next few years so why not teach yourself how to build one from scratch?

2. Machine Learning Use Cases in Transportation

The application of machine learning in the transport industry has gone to an entirely different level in the last decade. This coincides with the rise of ride-hailing apps like Uber, Lyft, Ola, etc.

These companies use machine learning throughout their many products, from planning optimal routes to deciding prices for the rise we take. So let’s look at a few popular use cases in transportation which use machine learning heavily.

 Dynamic Pricing in Travel

Do you often get frustrated by the surge pricing cab-hailing companies use? I encounter it on a daily basis for my commute to and from work. Prices seem to be perpetually hiked. Why is this happening?!So I dug into this a bit more and came across the concept of dynamic pricing – an excellent machine learning use case. To understand this, let’s take a simple example.

Google Maps

You must have guessed this one by now. Google Maps is a prime example of a machine learning use case. In fact, I would recommend opening up Google Maps right now and picking out the different features it offers.Here are some that I can see (and have used extensively):

• Routes: Go from point A to point B
• Estimated time to travel this route
• Traffic along the route
• The ‘Explore Nearby’ feature: Restaurants, petrol pumps, ATMs, Hotels, Shopping Centres, etc.

Google uses a ton of machine learning algorithms to produce all these features. Machine learning is deeply embedded in Google Maps and that’s why the routes are getting smarter with each update.

The estimated travel time feature works almost perfectly. If it shows ’40 minutes’ to reach your destination, you can be sure your travel time will be approximately around that timeline. Got to love machine learning!

LinkedIn and Facebook recommendations and ads

Social media platforms are classic use cases of machine learning. Like Google, these platforms have integrated machine learning into their very fabric. From your home feed to the kind of ads you see, all of these features work thanks to machine learning.A feature which we regularly see if ‘People you may know’. This is a common feature across all social media platforms, Twitter, Facebook, LinkedIn, etc. These companies use machine learning algorithms to look at your profile, your interests, your current friends, their friends, and a whole host of other variables.

4. Machine Learning Use Cases in Sales and Marketing

Top companies in the world are using machine learning to transform their strategies from top to bottom. The two most impacted functions? Marketing and Sales!

These days if you’re working in the marketing an sales field, you need to know at least one Business Intelligence tool (like Tableau or Power BI). Additionally, marketers are expected to know how to leverage machine learning in their day-to-day role to increase brand awareness, improve the bottomline, etc.

So, here are three popular use cases in marketing and sales where machine learning is changing the way things work. 

Recommendation Engines

We briefly spoke about recommendation engines earlier. I mentioned that these systems are ubiquitous. But where are they used in the marketing and sales field? And how?

Let’s take a simple example to understand this. Before the advent of IMDb (and Netflix), we all used to go to DVD stores or rely on Google to search for movies to watch. The store clerk would offer suggestions on what to watch and we took a hail mary pass by picking up movies we had no idea about.

HOW Intel USE Machine Learning ?

Data processing for artificial intelligence or machine learning applications is said to be slightly different from other types.

Whatever the details, experts say that a new generation of chips being built will be more capable of running AI and machine learning apps.

Still the largest chipmaker in the world, Intel clearly does not want to miss out on this nascent market, and has been active in acquisitions.

One of the largest purchases Intel made in the past few years is the $400 million acquisition of Nervana Systems, which build chips for data centre servers.

Nervana chips are said to be able to transfer data in and out at 2.4 terabytes per second at very low latency. That is supposed to be five to 10 times faster than the fastest input-out interfaces for traditional chips, according to a report on Forbes.

Similarly fast chips for AI have been released by Google and Nvidia, among others, so this could be a critical area of competition for Intel. Amazon is also said to be developing a custom chip for its Alexa AI system, according to TheVerge.com.

Intel Maps Out a Foldable, AI-Infused PC Future

The company's latest chips—and the bending gadgets they power—are learning to think for themselves.

THE IDEA THAT PCs are dying never held much weight; if anything, despite inroads by the iPad Pro, they’ve solidified their position as the device you turn to when you need to get things done. But where do they go from here? And with Moore’s Law in the rearview how will they continue to improve?

At this year’s CES, Intel is laying out a vision for what PCs might look like, and how they’ll act, going forward. At a glance, it sounds not unlike the future, and present, of smartphones: folding screens, artificial intelligence, and a dollop of Chrome.

The most immediate of those is AI, because it’s already here; Intel’s latest generation of Core processors, called Ice Lake, have begun shipping in PCs with AI-friendly features on board. Primary among these is Intel Deep Learning Boost, which accelerates on-device inference, the process by which algorithms interpret new data based on their training. As detailed at the company’s CES keynote, Intel’s upcoming Tiger Lake chips have the sort of performance gains you would expect—a "double digit" performance boost, Thunderbolt 4 integration, a new graphics architecture—but also a healthy improvement in how it handles AI tasks.

Artificial intelligence "is now equivalent and on par with all of the critical aspects of the platform,” says Gregory Bryant, Intel’s general manager of client computing. “It’s a first-class citizen in terms of driving our road map and our innovation, research and development, engineering, our partnership with developers.”

"I would not be surprised if 80 percent of software workloads, in some way or another, have some sort of AI acceleration built into them."

ROGER CHANDLER, INTEL

Given how enthusiastically AI gets thrown around, accurately or not, it’s important to have some context as to what all that emphasis will actually accomplish. That’s especially so in the case of AI capabilities at the chip level. After all, you interact with neural networks all the time on your PC already; it’s just that most of that work takes place in the cloud. Bringing it to the device offers all sorts of tangible benefits, even if not entirely transformative ones.

“If you’re going from your device to the cloud and back again, even at the speed of light, in some usages it introduces this lag that is very annoying,” says Roger Chandler, vice president of Intel Architecture, Graphics, and Software. Chandler notes also that running AI on the device means it’s both always available—no network disconnects to slow you down—and more private, since you don’t have to jettison your data to some far-flung server to get things done.

Take Adobe, which showed off its Ice Lake AI implementation onstage Monday. The capabilities have seeped across its software suite for creative tasks, speeding up everything from intelligent object selection to color matching to reframing a video. “We can use accelerated inference and machine learning, these algorithms, to do things that used to take minutes or longer in seconds,” says Gregory Bryant, Intel’s general manager of client computing. “The software can now make these usages possible automatically.”Adobe makes for an obvious partner here; the company has invested in AI integrations for several years through its Sensei framework. But it’s far from the only one. Chandler points to clinical lab company Quest Diagnostics, which he says has seen a 33X improvement in identifying lung nodules in CAD models, and photo software company Topaz Labs, which uses AI to upscale photo resolution by automatically filling in pixels. He’s optimistic that most of the software industry will soon follow suit.

“Over the next couple of years, I would not be surprised if 80 percent of software workloads, in some way or another, have some sort of AI acceleration built into them,” he says. “Some of them may have it as a core, foundational element of what an application does. Some of it might be small features. But when we talk to developers, pretty much all of them are doing something right now to leverage the ability of AI to improve their workload in some way.”

Which is perhaps unsurprising, given that this sort of on-device AI has already become relatively commonplace on smartphones. Apple introduced its Core ML framework in 2017, letting apps run neural networks on the iPhone and iPad. And Google emphasized its efforts to run Google Assistant locally on Android devices at Google I/O this year.

“There is no question that the ability to process data at the edge and not only in the cloud will play a big role in our future,” says Carolina Milanesi, an analyst at Creative Strategies.(It also has machine-learning chips of its own design.) No reason to leave PCs behind.

That applies to form factors as well. Just as smartphone makers are now tinkering with foldable displays—with limited success, although Microsoft’s take looks promising—PC stalwarts like Lenovo and Asus have brought to CES hardware with both dual and bending screens. Both appear to have learned the most important lesson from previous failed attempts at dual-display computers, which have sporadically surfaced for at least a decade: Make sure you still give people a physical keyboard to type on.

Intel itself introduced a reference device that it calls Horseshoe Bend. Intended as a sort of North Star for manufacturers, the concept takes up as much space as a 12-inch laptop, with a touchscreen that opens up to 17 inches. It’s not something you’ll ever see on a Best Buy shelf, but as much as anything else it indicates how unsettled the PC f“It’s too early to call, I think, exactly what types of sizes and formats are going to be successful,” says Intel’s Bryant. “But I’m convinced that these kind of dual-screen, foldable, more converged, mobile, more immersive-type devices are a segment that is emerging and that we need to address.”

And then there are existing form factors that could use a boost; in this case, that means Intel-powered Chromebooks. Samsung and Asus both announced models at CES that fall under Intel’s Project Athena designation, which means they tick off user-friendly boxes like all-day battery life and instant-on boot-ups. Twenty-five PC designs have gotten a Project Athena stamp of approval since Intel announced the initiative last year. Bringing Chromebooks into the fold helps ensure that both platforms get the same attention.