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Microsoft Introduces FPGA Technology atop Google Chips through Project Brainwave

Posted on May 10, 2018May 23, 2020 by Dexlab

A Change Is In the Make – due to increasing competition among tech companies working on AI, several software makers are inventing their own new hardware. A few Google servers also include chips designed for machine learning, known as TPUs exclusively developed in-house to ensure higher power and better efficiency. Google rents them out to its cloud-computing consumers. Of late, Facebook too shared its interest in designing similar chips for its own data centers.

However, a big player in AI world, Microsoft is skeptical if the money spent is for good – it says the technology of machine learning is transforming so rapidly that it makes little sense to spend millions of dollars into developing silicon chips, which could soon become obsolete. Instead, Microsoft professionals are pitching for the idea of implementing AI-inspired projects, named FPGAs, which can be re-modified or reprogrammed to support latest forms of software developments in the technology domain. The company is buying FPGAs from chip mogul, Intel, and already a few companies have started buying this very idea of Microsoft.

This week, Microsoft is back in action with the launch of a new cloud service for image-recognition projects, known as Project Brainwave. Powered by the very FPGA technology, it’s one of the first applications that Nestle health division is set to use to analyze the acuteness of acne, from images submitted by the patients. The specialty of Project Brainwave is the manner in which the images are processed – the process is quick as well as very low in cost than other graphic chip technologies used today.

It’s been said, customers using Project Brainwave are able to process a million images in just 1.8 milliseconds using a normal image recognition model for a mere 21 cents. Yes! You heard it right. Even the company claims that it performs better than it’s tailing rivals in cloud service, but unless the outsiders get a chance to test the new technology head-to-head against the other options, nothing concrete can be said about Microsoft’s technology. The biggest competitors of Microsoft in cloud-service platform include Google’s TPUs and graphic chips from Nvidia.

At this stage, it’s also unclear how widely Brainwave is applicable in reality – FPGAs are yet to be used in cloud computing on a wide scale, hence most companies lack the expertise to program them. On the other hand, Nvidia is not sitting quietly while its contemporaries are break opening newer ideas in machine learning domain. The recent upgrades from the company lead us to a whole new world of specialized AI chips that would be more powerful than former graphic chips.

Latest reports also confirm that Google’s TPUs exhibited similar robust performance similar to Nvidia’s cutting edge chips for image recognition task, backed by cost benefits. The software running on TPUs is both faster and cheaper as compared to Nvidia chips.

In conclusion, companies are deploying machine learning technology in all areas of life, and the competition to invent better AI algorithms is likely to intensify manifold. In the coming days, several notable companies, big or small are expected to follow the footsteps of Microsoft.

For more machine learning related stories and feeds, follow DexLab Analytics. It is the best data analytics training institute in Gurgaon offering state of the art machine learning using python courses.

The article has been sourced from – https://www.wired.com/story/microsoft-charts-its-own-path-on-artificial-intelligence

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Python Machine Learning is the Ideal Way to Build a Recommendation System: Know Why

Posted on May 9, 2018May 9, 2018 by Dexlab

In recent years, recommendation systems have become very popular. Internet giants, like Google, Facebook and Amazon, use algorithms to tailor search results to customer preferences. Any system that has a search bar collects data on a customer’s past behavior and likings, which enable these platforms to provide relevant search results.

All businesses need to analyze data to give personalized recommendations. Hence, developers and data scientists are investing all their energies and mental faculties to come up with perfect recommendation systems. Many of them are of the opinion that Python Machine Learning is the best way to achieve this. Often, building a good recommendation system is considered as a ‘rite of passage’ for becoming a good data scientist!

Delving into recommendation systems:

The first step in the process of building a recommendation system is choosing its type. They are classified into the following types:

Recommendation based on popularity:

This is a simplistic approach, which involves recommending items that are liked by the maximum number of users. The drawback of this approach is its complete exclusion of any personalization techniques. This approach is extensively used in online news portals. But in general, it isn’t a popular choice for websites because it bases popularity on entire user pool, and this popular item is shown to everyone, irrespective of personal choice and interest.

Recommendation based on algorithms:

This process uses special algorithms that are tailor-made to suit every customer. They are of two types:

Content based algorithms:

These algorithms are based on the idea that if a person likes a product then he/she will also like a similar product. It works efficiently when it is possible to determine the properties of each product. It is used in movie and music recommendations.

Collaborative filtering algorithms:

These algorithms are dependent on past behavior and not on properties of an item. For example, if a person X likes items a, b, c and another person Y likes items b, c, d, then it is concluded that they have similar interests and X should like item d and Y should like item a. Because they are not dependent on additional information, collaborative filtering algorithms are very popular. E-commerce giants, like Amazon and Flipkart, recommend products based on these algorithms.

After choosing the type of recommendation system to build, developers need to locate relevant datasets to apply to it. The next step is determining the platform where you’ll build your recommendation system. Python machine learning is the preferred platform.

Advantages of using Python Machine Learning:

Code: Python makes the process of writing code extremely easy and working with algorithms becomes quite convenient. The flexible nature of this language and its efficiency in merging different types of data sets make it a popular choice for application in new operating systems.
Libraries: Python encompasses a wide range of libraries in multiple subjects, such as machine learning and scientific computing. The availability of a large number of functions and methods enables users to carry out several actions without having to write their own codes.
Community: Python includes a large community of young, bright, ambitious and helpful programmers. They are more than willing to provide their valuable inputs on different projects.
Open source: The best part about Python is that it is completely open source and has sufficient material available online that will help a person develop skills and learn essential tips and tricks.

Proficiency in Python is highly advantageous for anyone who wants to build a career in the field of data science. Not only does it come handy in building complicated recommendation systems, it can also be applied to many other projects. Owing to its simplicity, Python Machine Learning is a good first step for anyone who is interested in gaining knowledge of AI.

In the current data-driven world, knowing Python is a very valuable skill. If one’s aim is to collect and manipulate data in a simple and efficient manner, without having to deal with complicated codes, then Python is the standard.

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Artificial Intelligence: Let’s Crack the Myths and Unfold the Future to You

Posted on August 3, 2017December 11, 2018 by Dexlab

Artificial Intelligence: Let’s Crack the Myths and Unfold the Future to You

A lot of myths are going around about Artificial Intelligence.

In a recent interview, Alibaba founder Jack Ma said AI can pose a massive threat to jobs around the world, along with triggering World War III. The logic of shared by him explained that in 30 years, humans will be working for only 4 hours a day, and 4 days a week.

Fuelling this, Recode founder Kara Swisher vouched for Ma’s prediction. She supported him by saying Ma is “a hundred percent right,” adding that “any job that’s repetitive, that doesn’t include creativity, is finished because it can be digitized” and “it’s not crazy to imagine a society where there’s very little job availability.”

Besides, I find all these stuffs quite baffling. I think that if AI is going to be the driving force towards innovation and bringing in a new technological revolution, it’s upon US to curate the opportunities that will require new jobs. Apocalyptic predictions just don’t help.

Let’s highlight the myths and the logical equations:

Myth 1: AI is going to kill our jobs – it can never happen

Remember, it’s humans who have created robots. We excel at mechanizing, systematizing and automating. We spurred the automation drive, while infusing intelligence to the machines.

The present objective is to create AIs that can work together with human intelligence to develop new narratives for problems we are yet to solve. To solve these new problems, we need new kinds of jobs – there’s a great scope of opportunity, let’s not believe that AI will kill our jobs.

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Myth 2: Robots are AI. Not at all.

From drones to self-organizing shelves in warehouses to machines sent to Mars, all are just machines programmed to function.

Myth 3: Big Data and Analytics are AI. Who said that?

Data mining, Data Science, Pattern Recognition – they are just human-created models. They might be intricate or complicated in nature, but not AI. Data and AI are two entirely different and divergent concepts.

Myth 4: Machine Learning and Deep Learning are AI. Again a big NO.

Though Machine Learning and Deep Learning are a part of the enormous AI tool kit, they are not AI. They are just mere tools to program computers to tackle complex patterns- like the way your email filters out spam by “understanding” what hundreds and thousands of users have identified as spam. They look uber smart, undeniably, in fact scary at times, when a computer wins against a renowned expert at the game GO, but they are definitely not AI.

Myth 5: AI includes Search Engines. Definitely NO.

Search Engines have made our lives easier, undoubtedly. The way you can search information now was impossible few years back, but being the searcher, you too contribute the intelligence. All the computer does is identify patterns from what you search and suggest it to others. From a macro perspective, it doesn’t actually know what it finds because it’s dumb in the end. We feed them intelligence, otherwise they are nothing.

So, instead of panicking about the uncertainties that AI may bring into our lives, we should take a bow and appreciate the efforts humans gave into creating something so huge, so complex like AI.

And remember, AI has always created jobs in the past and didn’t take them. So, be hopeful!

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Indian Startups Relying on Artificial Intelligence to Know Their Customer’s Better

Posted on July 19, 2017October 1, 2018 by Dexlab

Artificial Intelligence was there decades ago, but everyone is talking about AI and Big Data in India’s startup ecosystem of late.

Budding startups are looking for new talent with AI expertise to inspect and evaluate consumer data and provide customized services to the users. At the same time, tech honchos such as Apple have discovered the huge potentials hidden within Indian companies that help their clients with data processing, image and voice recognition, and no wonders, investors are too hopeful for Indian AI startups.

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Here are a slew of Indian unicorns – companies valued at $1 billion or more that are putting in use the exploding technology of AI in the best way possible:

Paytm

An eye-piercing transformation from being an e-wallet to selling flight or movie tickets, Paytm is now implementing machine learning to bring order into chaos. The company’s chief technology offer, Charumitra Pujari, said, “You could Google and try to look for something. But a better world would be when Google could on its own figure out Charu is looking for ‘x’ at this time. That’s exactly what we’re doing at Paytm,” he further added, “If you’ve come to buy a flight ticket, because I understand your purchase cycle, I show that instead of a movie ticket or transactions.”

In order to identify and prevent fraudulent activities, machines are constantly assessing illicit accounts that purposefully sign up to derive advantage of promo codes, or for money laundering intention. The fraud-detection engine is extremely efficient, leaving no room for human error, Pujari stated.

The team at Paytm is versatile – machine learning engineers, software engineers, and data scientists are in action in Toronto, Canada, as well as in Paytm’s headquarters in Noida, India. Currently, they have 60 people working for them in each location – “We know the future is AI and we will need a lot more people,” said Pujari.

Ola cabs

One of the most successful ride-hailing apps in India, Ola uses machine learning tech to track traffic, crack through driver habits, improve customer experience and enhance the life of each vehicle they acquired. AI plays a consequential role in interpreting day-in-day-out variations in demand and to decipher how much supply is required to cater to its increased demand, how variable are traffic predictions and how rainfall affects the productiveness of vehicles.

“AI is understanding what is the behavioral profile of a driver partner and, hence, in which way can we train him to be a better driver partner on (the) platform,” co-founder and chief technology officer Ankit Bhati said, the algorithms put into the car-pooling service works great in pulling down travel times by coordinating with various pick-up points and destinations, while sharing one single vehicle, he further added.

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Flipkart

According to a report in Forbes, Flipkart – India’s largest domestic e-commerce player has already re-designed its app’s home screen to give a more personalized version of services to its mushrooming 120 million patrons. Machine learning models crack each customer’s gender, brand preference, store affinity, price range, volume of purchases and more. In fact, in future, the company is going forward to figure out the reasons about when and why the returns are made, and as a result will try to reduce their happenings.

A squad of 25 data scientists at Flipkart have started using AI to observe the past buyer behavior to predict their future purchases. “If a customer keys in a query for running shoes, we show only the category landing pages of the particular brand the customer wants to see, in the price point and styles that (are) preferred, as gauged by previous buying behaviour, therefore ensuring a faster, smoother checkout process,” Ram Papatla, the vice president of product management at Flipkart, said recently at an interview with a leading daily.

ShopClues, InMobi, SigTuple and EdGE Network are myriad other Indian startup players who are making it really big by utilizing the powerful tentacles of AI and machine learning.

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Role of Self Service Analytics in Businesses

Posted on April 5, 2017May 23, 2020 by Dexlab

Self Service Analytics is proving useful for business users, who are working on business data without necessarily having a background in technology and statistics. It is essentially bridging the gap between trained data analysts and normal business users.

Following are the characteristics of Self Service Analytics:

Business Users Independence:

Self Service Analytics reduces dependency on IT and Data warehousing teams, thereby reducing the turnaround time for a request made by a business user.

It does so by continuously collating and loading real time data into a singular stream without disparity, which is easily accessible through browsers. Thus, it helps business users in taking decisions on Real-Time basis.

This feature benefits organizations because vital decisions made within time can be more profitable as compared to the traditional way of analysing data, which may not be a good idea in respect to the urgency constraint.

Easier and Reduced Cost of Operations:

Often, the company’s data are fragmented and widespread across various divisions. This increases the headache of channelling the data meaningfully and in a wholesome manner.

Further to this, preparing reports using this data becomes a cumbersome job for the IT department or the department, which is serving such request. Hence, it may lead to increased cost of time or decreased quality of efficiency at which the operations have to run. However, many a times, these reports fail to give an overview of the operations in an organisation.

Self-service BI integrates data from different systems and delivers a “Single Version of Truth”. Accessing this data and running computations on it requires only a browser for access and eliminates the need to install, maintain and administer large-footprint software clients on each user’s workstation.

If Self Service Analytics is hosted on SaaS, it will further reduce the cost of machinery and maintenance associated with it. The provision for usage can be increased or decreased in no time according to the usage pattern. This really means that Self Service Analytics helps you adapt with time and Pay-Per-Use model, which is a leading trend in most of the industries.

Resolving the conflict over accuracy:

Typically, a business user using Excel would have a local copy of data and run computations on it. He can merge and transform it by using various formulas and finally derive a conclusion.

This is dangerous because in live operations, data keeps changing and data integrity is at stake by working on local copies. Thus, accuracy in decision-making becomes a game of luck.

In Self Service BI, the data from the source is extracted, transformed and loaded into a unique data model, which goes with all operations. In this case, data integrity is assured. In addition, all business users have the same source of data, removing the risk that working with different local copies have.

Therefore, from the above stated facts, we can conclude that Self Service Analytics is a need for today’s businesses.

However, there are a few risks involved in Self Service Business Analytics:

Loose corporate governance and make data available to business users directly may be taken advantage of in an undue manner.
Business users may not be properly trained or skilled to make decisions.
Relying heavily on any tool without some real life experience and insight into the background of that data can result into an impaired decision-making.

If all the above-mentioned risks are mitigated and proper corporate governance structure is in place, Self Service Analytics can be very beneficial for the success of any organization.

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Facebook is planning to evaluate its quest for generalised AI

Posted on March 13, 2017November 16, 2018 by Dexlab

A major misconception about artificial intelligence is the fact that today’s robots possess a very generalized intelligence, however, we are fairly efficient in leveraging large datasets to accomplish otherwise complex tasks. Nevertheless we still fail and fall flat at the prospect of replicating the breadth of human intelligence.

Care to contribute to AI development in today’s world? Then take up a Machine Learning course online with us. But in order to move forward a generalized intelligence, Facebook is ensure that we know how to evaluate the process. In a recently released paper, Facebook’s AI research (FAIR) lab has outlined just that as a part of its CommAI framework.

We will need our systems to be able to communicate and will be able to learn through language effectively even when they lack in context and discussing thing in undefined terms.

Furthermore, such systems should be capable of learning up new skills, fairly simply. As per Facebook this skill set is called “learning to learn”. Present machine learning models may be trained on data and be used for classifying defined objects. We can also make use of transfer learning to quickly adapt a model to achieve the same task on the new data, however our machines cannot completely teach themselves without heavy to moderate intervention from the developers.

It is in general agreed upon, that in order to generalize across several tasks, a program should be capable of compositional training. And that is of storing and recombination solutions to sub-problems across the different tasks, as per the team from Facebook.

As per Facebook they consider these capabilities to be of more of a prerequisite to being a generalized AI than the true Turing test. Alan Turing created the original Turing test in the 1950s. It is usually understood to be a means of assessing machine learning intelligence with respect to human intelligence.

However, with the maturation of the field of Ai the Turing test has lost a lot of its relevance. Facebook hopes to offer a nice alternative way to think about the necessary requirements of a modern generalized AI which should be less of a research distraction than the more rigid Turing Test.

The team at FAIR which include – Marco Baroni, Armand Joulin, Allan Jabri, Germán Kruszewski, Angeliki Lazaridou, Klemen Simonic and Tomas Mikolov have also developed another open source platform for the testing and training of AI systems.

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