Bengaluru, June 13 (IANS) Amazon India on Sunday announced the launch of ML Summer School which will provide an integrated learning experience for students to gain applied Machine Learning (ML) skills.

A batch of students from select tech campuses in India will be presented with the opportunity to engage through virtual classroom tutorials followed by interactive Q&A sessions with scientists at Amazon.

For students with prior exposure to certain areas of ML, the programme can act as a refresher course, while additionally providing a practical perspective on ML applications in industry, the company said in a statement.

“With the pace of advancements in ML, we are proactively helping students to learn about the latest trends in the field of ML and apply them to solve real-world problems,” said Rajeev Rastogi, VP, India Machine Learning at Amazon.

“Our aim is to prepare students for science roles — this will help to reduce the gap between the growing demand for ML roles across companies and the talent pool with applied ML skills,” he added.

ML Summer School is open to engineering students in their pre-final/final year of Bachelors, Masters or PhD studies across select tech campuses in 2021.

Participants of ML Summer School will be identified through an online assessment.

They will also have access to the Amazon Research Days (ARD) conference where they can learn about technology trends in industry through presentations from renowned ML leaders around the world.

The curriculum of ML Summer School will cover the fundamental concepts in ML while linking them to practical industry applications through an immersive three-day course.

Students will get to learn first-hand on how advanced ML techniques such as Deep Learning and Probabilistic Graphical Models can be leveraged to solve specific business problems in the e-commerce domain such as demand forecasting, catalogue quality, product recommendations, search ranking and online advertising.

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The programme will allow participants to build ML models for autonomous driving applications, the company said in a statement on Monday

In a bid to help women students learn machine learning (ML) via a Cloud-based virtual racing simulator, Amazon Web Services (AWS) has launched the DeepRacer Women’s League-India 2021.

The programme will allow participants to build ML models for autonomous driving applications, the company said in a statement on Monday.

The event will begin with a regional elimination round in four regions (north, east, west, south), followed by a national elimination community race.

The grand finale is on April 21 that will allow participants to upload their models and run on the virtual track, which will be streamed live on twitch.tv.

The winner of the India event will participate in the International DeepRacer League, providing an opportunity to acquire and test their ML skills on a global platform.

As an autonomous racing league, the initiative offers a unique opportunity to compete on tracks virtually while diving deep into advanced reinforcement learning and upskilling with AI and ML.

Participants will have the opportunity to leverage the AWS machine learning and AWS DeepRacer course modules and several enablement sessions planned during the event.

“The programme is open for all female students currently enrolled with higher education academic institutions in India, and is designed to promote upskilling in machine learning (ML), as well as inclusion and diversity in the technology sector,” said AWS, the Cloud arm of Amazon.

According to a global study, India has a higher-than-average share of women with AI skillsets (at 22 per cent) compared to other countries.

“This event is expected to foster community learning and help with the early adoption of artificial intelligence and machine learning technology for a more diverse workforce of the future,” the company said.

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Amazon India has notified of a data science internship available in Bangalore, Karnataka. Applications have been invited for the same from candidates with an academic and/or practical background in computer science, engineering, operations research, or process control. Selected interns will be required to optimize one of the most complex logistics systems in the world, says the company.

Who can Apply for Data Science Internship in Amazon India

Candidates pursuing their Masters or equivalent advanced degree in computer science, computer engineering, statistics, mathematics or related technical discipline, and with the following experience can apply for the data science internship:

• Academic experience in manipulating or transforming data, model selection, model training, cross-validation and deployment at scale.
• Academic or project experience with machine and deep learning toolkits.
• Academic experience with big data platforms like Apache Spark and Hadoop.
• Familiarity with data processing with Python, R and SQL.
• Familiarity with AWS services related to artificial intelligence and machine learning, particularly Amazon EMR, AWS Lambda, SageMaker, Machine Learning, IoT, Amazon DynamoDB, Amazon S3 and EC2 Container Service, Green Grass and so on.

Candidates will also have to be fluent in written and spoken English, should have excellent communication and data presentation skills, and should also have a strong working knowledge of Excel and Microsoft Office skills.

Responsibilities that Selected Candidates will have to Fulfill at Amazon India

Some of the major responsibilities include:

• Using data analyses and statistical techniques to develop solutions to improve customer experience and to guide business decision making.
• Identifying predictors and causes of business-related problems and implement novel approaches related to forecasting and prediction.
• Identifying, developing, managing, and executing analyses to uncover areas of opportunity and present written business recommendations.

Interested candidates will have to apply using their Amazon account. Candidates who wish to apply are advised to visit the official website for more information.

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Underground cloud services may seem like an oxymoron, but they are quite real, and criminals are using them to speed up attacks and leave very little room for compromised businesses to react.

This is according to a new report from cybersecurity firm Trend Micro, which found terabytes of internal business data and logins – including for Google, Amazon and PayPal – for sale on the dark web.

The logins are sold through access to the cloud logs where they’re stored. As a result, Trend Micro argues, more accounts are monetized and the time from compromise to the account actually being used for nefarious purposes is cut from weeks to days or hours.

Just as businesses enjoy the speed and scalability of cloud services, so do criminals; more computing power and bandwidth allows them to optimize their operations.

Criminals that buy the logs of cloud-based stolen data usually use the data for the purposes of secondary infection, with ransomware being one of the more popular choices.

The report argues that this is a new trend that may gain even more popularity in the future, and even create a “new type of cybercriminal”: an expert in data mining that uses machine learning to enhance pre-processing and extraction of information to maximize usefulness to potential buyers.

Trend Micro believes criminals will focus on standardizing their services and pricing.

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Years ago, Google struggled with how to pitch its cloud offerings. Back in 2017 I suggested that the company should help mainstream enterprises to “run like Google,” but in a conversation with a senior Google Cloud product executive, he suggested that the company shied away from this approach.

The concern? That maybe mainstream enterprises didn’t share Google’s needs, or maybe Google would simply intimidate them.

For the mere mortals that run IT within such mainstream enterprises (read: almost everyone), fear not. It turns out there are many things that Google might do that make no sense for your own IT needs.

Just ask Colm MacCárthaigh, AWS engineer and one of the authors of the Apache HTTP Server, who asked for “examples of technical things that don’t make sense for everyone just because Amazon, Google, Microsoft, Facebook” do them. The answers—excessive uptime guarantees, site reliability engineering, microservices, and mono-repos among the highlights—are instructive.

Excessive uptime guarantees

“Five or five-plus nines availability guarantees,” says Pete Ehlke. “Outside of medicine and 911 call centres, I can’t think of anything shy of FAANG [Facebook, Amazon, Apple, Netflix, and Google] scale that actually needs five nines, and the ROI pretty much never works out.”

I remember this one well from the variety of start-ups for which I worked, as well as when I was at Adobe (whose service-level commitments tend not to be five nines, but are arguably higher than necessary). Are you going to be OK if the multi-player game goes down? Yep. What about Office 365 for a few minutes, or even hours? Yes and yes.

Site reliability engineering

A bit of a spin on devops (though it predates the devops movement), SRE (named in multiple replies to MacCárthaigh) came out of Google in 2003, and was designed to infuse engineering with an operational focus. A few core principles guide SRE:

• Embrace risk
• Utilize service level objectives (SLOs)
• Eliminate toil
• Monitor distributed systems
• Leverage automation and embrace simplicity

Or, as Ben Traynor, who developed Google’s SRE practice, describes it:

SRE is fundamentally doing work that has historically been done by an operations team, but using engineers with software expertise and banking on the fact that these engineers are inherently both predisposed to, and have the ability to, substitute automation for human labour. In general, an SRE team is responsible for availability, latency, performance, efficiency, change management, monitoring, emergency response, and capacity planning.

SREs spend much of their time on automation, with the ultimate goal being to automate away their job. They spend considerable time on “operations/on-call duties and developing systems and software that help increase site reliability and performance,” says Silvia Pressard.

This sounds important, and even more so if you equate “site reliability” with “business availability.” But do most companies really need their developers to become operational experts? SRE might be critical at Google or Amazon, but it’s arguably a heavy lift for most enterprises, tasking developers with too much of an operational load for them to manage it successfully.

Microservices architecture

As commentator “Buzzy” tells it, “Definitely microservices. The number of 20-staff-in-total companies I’ve had to talk down from that ledge….” Nor is he the only one to call out microservices as a needless complication for most enterprises. Many of the replies to MacCárthaigh’s tweet mentioned microservices.

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Since the dawn of Bronze age civilizations more than 5000 years ago, humans have been creating norms of societal governance. The process continues with many imperfections. Off late, Artificial Intelligence (AI) is increasing its influence in decision making processes in the lives of humans and expectations are whether AI will follow similar or better norms. Principles that govern the behaviour of responsible AI systems are being established.

Principles

Fair

All AI systems should be fair in dealing with people and be inclusive in coverage. In particular, they should not show any bias in working. Historically, humans have used at least 2 major criteria for unfair treatment, i.e. gender and caste/race/ethnicity. 

Amazon tried to develop an algorithm for recruitment. However, it started showing less tendency to select female candidates. Even after removing gender specific indicators, females were still discriminated against. The project had to be abandoned.

Compas, a risk-assessment tool developed by a privately held company and used by the Wisconsin Department of Corrections predicted that people of colour had higher tendency of repeat offences than they actually do. California has decided not to use face recognition technology for law enforcement. A study by Stanford researchers in 2020 found that voice recognition software of Amazon, Apple, Google, IBM, and Microsoft have higher error rates when working on voice of black people.

Transparent and Accountable

Unlike traditional software, it is hard to predict the outcome of AI algorithms as they dynamically change with training. This makes them less transparent and this “Black box” nature of AI makes it very difficult to find the source of error in case of a wrong prediction. This also makes it makes difficult to pinpoint accountability. Neural networks are the underlying technology for many face, voice, character etc recognition systems. Unfortunately, it is more difficult to trace problems in neural networks especially deep ones (with many layers) than in other AI algorithms e.g decision trees etc. And new variants of neural networks e.g. GANs (Generational Adversarial Networks), Spiking Neural Networks etc continue to gain popularity.

Reliable and safe

Security and reliability of AI systems has certain peculiar dimensions e.g. unpredictability. Facebook in collaboration with Georgia Institute of Technology created bots that could negotiate but they also learnt how to lie. This was not intended during programming. Another issue is slow rise of Artificial General Intelligence (AGI) or Broad AI or Strong AI that aims to create systems that genuinely simulate human reasoning and generalize across a broad range of circumstances. These algorithms will be able to do transfer learning, so an algorithm that learns to play Chess will also be to able learn how to play Go. This will vastly increase the context in which a machine can operate and this cannot be predicted in advance.

Unpredictability reduces reliability and safety of the systems.

Problem sources

Models and features

The power of AI algorithms is based on the models and features and the weightages of the features that are used while creating models. The AI in use currently is also Narrow AI and it will not work if the context changes. For example, a system designed to scrutinize applications for medical insurance policies may discriminate against people with diseases if used to vet applications for car insurance since the features and their weightages are not appropriate for the latter case. Hence models or features framed without fairness in mind can induce biases.

Data

The biggest source of biases in AI systems is data as biases may be inherent in the data, either explicitly or subconsciously. This can happen if data is not uniformly sampled or carries implicit historical or societal biases. In credit risk, data of customers who defaulted less as they were supported by tax benefits will give incorrect results when used for scenarios where tax benefits are not there. MIT researchers found that facial analysis technologies had higher error rates for minorities and particularly minority women, potentially due to unrepresentative training data. The reason for failure for Amazon recruitment software was that it was trained on 10 years of data where resumes of male candidates outnumbered that of females. It also focussed on words e.g. “executed”, “captured” etc that are more commonly used by males.

Other issues

The rise of AI poses additional challenges not found in traditional systems.

Driverless Vehicles

The driverless vehicles will start plying on the roads in a decade or so. Any accident will raise the question of civil and criminal liability. In 2018 a pedestrian died when she was hit by Uber test car despite a human driver sitting inside the car. A vehicle may be programmed to save either the passengers or pedestrians. Potential accused could be vehicle manufacturer, vehicle operator or even the government. This will also change the underwriting models. Liability issues will also come as companies allow operation decisions to be more data driven as now programmers will appear to be the sole accused. 

Weapons

Countries e.g. US, Russia, Korea etc plan to use AI in weapons e.g. drones or robots etc. Currently the machines do not have emotions and this raises the concern if an autonomous machine goes on killing spree. In 2018, Google had to stop engagement with US government over its Maven military program due to public outcry.

Safeguards

Guidelines

The concerns over ethics in AI have resulted in many organizations formulating guidelines governing the use of AI e.g. European Commission’s, “Ethics Guidelines for Trustworthy Artificial Intelligence”,  US government’s “Roadmap for AI Policy”, IEEE’s P7000 standards projects etc. These contain the general principles of ethics and responsibility that AI systems should follow.

Software

Many companies have created frameworks, software, guidelines etc that can help to create Responsible AI e.g. IBM, Google, Microsoft, PWC, Amazon, Pega, Arthur, H2O etc. Their software help to explain model’s “Black box” behaviour and hence bring transparency, assess fairness of the systems, mitigate bias against any identity based groups, keep the data secure etc by constant monitoring.

Companies

Within companies, Responsible AI can be facilitated by imposing standards through overseeing groups, creating diversity in teams and cascading the message to individuals. There should be conscious efforts to reduce biases in data.

Future

In the next two decades, machines will become more autonomous in decision making processes and human will slowly cede control of their own lives. Establishment of Responsible AI will reduce biases and increase acceptance of AI. This will help in creating a more fair and equitable society. An unchecked growth of AI will not only make humans less tolerant to AI but also to each other.

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Amazon’s new tool, CodeGuru, leverages machine learning to streamline the code-review process. But will it actually interest developers?

CodeGuru features two components: a “Reviewer” that uses machine learning to scan code for bugs, and a “Profiler” that highlights latency and CPU utilization issues. Profiler will also produce an “estimated dollar value” for the active CPU costs of an issue, allowing developers to optimize their code in order to save money.

Originally introduced at last year’s Amazon re:Invent conference, CodeGuru has spent the past few months undergoing a variety of modifications. There are a few caveats: As of right now, it seems that Reviewer’s bug-fixing recommendations are limited to Java code stored in GitHub, AWS CodeCommit, or Bitbucket. Profiler, meanwhile, supports applications written in Java virtual machine (JVM) languages such as Clojure, JRuby, Jython, Groovy, Kotlin, Scala, and (of course) Java.  

The official AWS blog features a pithy coding walkthrough using CodeGuru, including its visualization and recommendation features. As with Amazon Honeycode, the company’s new low-code tool, the corporate goal of CodeGuru seems pretty clear: Useful tools will compel developers to stick with AWS and Amazon’s ecosystem, and not potentially drift over to Microsoft’s Azure or Google’s Cloud options.

The big test, of course, is whether CodeGuru’s machine learning is capable of identifying bugs in a way that makes the coding process more efficient. Some code is the result of reasonable compromises that developers made earlier in their process, and it’s up in the air whether an artificial intelligence will necessarily recognize the creativity and larger issues behind those decisions. And while latency and resource utilization are huge issues, they’re impacted by a variety of factors that CodeGuru might not always realize.

Nonetheless, anything that potentially helps developers do their jobs is a good thing, and CodeGuru joins a growing number of developer tools that try to use machine learning and A.I. to make the coding process a little better. For example, you have code “autocompleters” such as Deep TabNine, IntelliSense (part of Visual Studio), and Kite, all of which attempt to leverage different flavors of machine learning to predict what a human coder will type next. There are also A.I. tools that attempt to measure everything from security to complexity.

In other words, CodeGuru isn’t a striking development. Nonetheless, it shows yet again that machine learning will only influence coding more and more as the years go on.

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Amazon Web Services Inc. said today its new Amazon CodeGuru service, which relies on machine learning to automatically check code for bugs and suggest fixes, is now generally available.

Amazon announced the tool in preview at its AWS re:Invent event in December.

“It’s challenging to have enough experienced developers with enough free time to do code reviews, given the amount of code that gets written every day,” the company said today. “And even the most experienced reviewers miss problems before they impact customer-facing applications, resulting in bugs and performance issues.”

AWS CodeGuru is actually made up of two separate tools, including a Reviewer and a Profiler, and they do pretty much what the names suggest.

To get started, developers must commit the code they want to review to a hosting repository such as GitHub, Bitbucket Cloud or AWS CodeCommit. CodeGuru Reviewer then kicks in by analyzing that code, searching for bugs and offering potential fixes for them. This is all done within the code repository itself, so for example, if the developer is using GitHub, CodeGuru will create a pull request and add a comment with specific information about the bug it has found and any potential fixes it recommends.

CodeGuru Reviewer has ample pedigree, as its algorithm was trained on code from more than 10,000 open-source projects hosted on GitHub. The algorithm should improve over time too, since developers can leave feedback on the bugs it finds and the recommendations it provides.

As for CodeGuru Profiler, it’s all about optimizing the code. The idea is to help developers find inefficiencies with their code and also the most expensive lines of code. The tool is supported on serverless platforms such as AWS Lambda and AWS Fargate.

In addition, CodeGuru Profiler now adds an estimated dollar amount to any unoptimized lines of code it finds.

“Our customers develop and run a lot of applications that include millions and millions of lines of code,” said Swami Sivasubramanian, vice president of Amazon Machine Learning. “Ensuring the quality and efficiency of that code is incredibly important, as bugs and inefficiencies in even a few lines of code can be very costly.”

Constellation Research Inc. analyst Holger Mueller said the general availability of Amazon CodeGuru shows that the time when coders would rely on integrated development environments to highlight code and find syntax errors is a thing of the past.

“Machine learning and artificial intelligence algorithms improve developer productivity, helping them to achieve what matters most to executives,” Mueller said. “That is, developer velocity to build next-generation applications.”

Amazon said several big companies are already using CodeGuru to find bugs in and optimize their code, including Atlassian Corp. Plc.

“While code reviews from our development team do a great job of preventing bugs from reaching production, it’s not always possible to predict how systems will behave under stress or manage complex data shapes, especially as we have multiple deployments per day,” said Zak Islam, head of engineering for tech teams at Atlassian. “When we detect anomalies in production, we have been able to reduce the investigation time from days to hours and sometimes minutes thanks to Amazon CodeGuru’s continuous profiling feature.”

Amazon CodeGuru is available now in the company’s US East (N. Virginia), US East (Ohio), US West (Oregon), EU (Ireland), EU (London), EU (Frankfurt), EU (Stockholm), Asia Pacific (Singapore), Asia Pacific (Sydney), and Asia Pacific (Tokyo) regions. The service will be made available in additional regions in the coming months, Amazon said.

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In October 2019, an Amazon employee in Melbourne, Australia bumped into another person while cycling on the road. As she was assuring that person that she would help, she realised that he was deaf and mute and had no clue on what she was saying.

The awkward situation could have been avoided if assistive technology was on hand to facilitate communication between the two parties. Following the incident, a team led by Santanu Dutt, head of technology for Southeast Asia at Amazon Web Services, got down to work.

Within ten days or so, Dutt’s team built a machine learning model that was trained on sign languages. Using images of a person gesturing in sign language that were captured from a camera, the model could recognise and translate gestures into text. The model also could convert spoken words into text for a deaf-mute person to see.

Dutt said the model can also be customised to translate speech into sign languages as the machine learning services and application programming interfaces (APIs) are available and open – though he has not seen that demand yet. “But once you write a small bit of code, training the machine learning model is easy,” he said.

There is still more work to be done. As the training was performed with signs gestured against a white background, the efficacy of the model in its current form would be limited in actual use.

“Our team had limited time to showcase this and we wanted to bump up something to showcase for experimental purposes,” Dutt said, adding that organisations can use tools such as Amazon SageMaker to edit and train the model with more images and videos to recognise a larger variety of environments.

As the training process is intensive, Dutt said organisations with limited resources can use Amazon SageMaker Ground Truth to build training datasets for such machine learning models quickly. Besides automatic labelling, Ground Truth also provides access to human labellers through the Amazon Mechanical Turk crowdsourcing service.

This will also help to improve the model’s accuracy rate. “The more data you have, the more accurate the model gets,” Dutt said, adding that developers can set confidence levels and reject results that fall below a certain level of accuracy.

Dutt said AWS’s public sector team has engaged non-profit organisations in Australia to conduct a proof-of-concept that makes use of the machine learning model, as well as those in other countries through credits that offset the cost of using AWS services to train and deploy the model.

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JavaScript remains the most popular programming language in the world, according to Amazon’s State of the Developer Nation Survey for 2019.

The language has more than 12 million users worldwide and is one of the fastest-growing programming languages globally – growing by 3 million developers between Q4 2017 and Q4 2019.

Amazon estimates that there are more than 20.4 million active software developers in the world, more than half of which are using JavaScript.

“Not only do new developers see it as an attractive entry-level language, but also existing developers are adding it to their skillset,” Amazon said.

“As a result, JavaScript is now used by more than half of developers working on web applications, cloud services, or extensions for third-party ecosystems.”

Two other languages that have seen steady growth are Python and Java.

Python added 2.2 million new developers in 2018 and surpassed Java in terms of popularity. Despite this growth slowing in 2019, Python remains the second-most widely-used programming language overall.

Popular applications

The survey found that JavaScript was most popular in web, cloud, and third-party ecosystem applications, and least popular in IoT app and gaming development.

Python was the most popular programming language for IoT applications and the least popular in gaming and mobile development.

Java, the third-most-popular language in the world, was most popular among mobile and cloud developers and was one of the least popular languages for web applications.

Programming languages which were popular for AR and VR applications included C#, Visual tools, Swift, and Rust.

C# was also most popular among video game developers for platforms other than mobile.

“The fastest-growing language community in percentage terms is Kotlin,” Amazon noted. “It nearly doubled in size in the past two years, from 1.1 million developers in Q4 2017 to 2 million in Q4 2019.”

“Given that Google has made Kotlin its preferred language for Android development, we can only expect this growth to continue, and Kotlin becoming a core language in mobile development.”

Open-source contributions

The survey also found that 3 out of 5 developers contribute to open-source software.

“Developers are most motivated to contribute to open-source projects to improve coding skills (29%) and a belief in the benefits of open source (26%),” Amazon said.

“What’s more, 22% of developers contribute to open-source software because it’s fun or to solve an issue with an existing open-source software project such as fixing a bug or creating a new feature.”

The graph below details the most popular programming languages in the world according to Amazon’s State of the Developer Nation Survey for Q4 2019.

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