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These are the people leading the way in AI development

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By Mazin Gilbert, AT&T

Artificial Intelligence (AI) has come a long way since its debut in Bell Labs in the 1950s. It’s transformed from a sci-fi concept to now being a reality in our data-driven world. And while the technology and applications may have changed over the decades, the underlying goal remains the same – enhance and simplify our everyday lives.

AI is expected to impact product development and innovation across all industries. Needless to say, these advancements wouldn’t be possible without people constantly working to solve problems, think outside the box, innovate and create forward-looking solutions.

But to advance AI faster, we needed to break down barriers and make it more accessible to developers, entrepreneurs and businesses.

We unveiled the Acumos AI Project nearly a year ago. We collaborated with Tech Mahindra to build Acumos, which is an open source AI platform under The Linux Foundation Deep Learning – an umbrella including 15 corporate members. And, we recently co-sponsored the first-ever Acumos AI Challenge with Tech Mahindra. We challenged students, developers and data scientists across the country to come up with AI solutions. The contest featured a total of $100,000 in cash awards.

We called for innovative, fresh ideas – and the industry answered. More than 300 developers participated, and submissions spanned industries including healthcare, media and entertainment, and security.

Entries were judged against 5 judging criteria categories:

  • Novelty & Originality
  • Viability & Impact on Market
  • Difficulty of Technical Implementation
  • Best Supports Package Requirements & Intended Function
  • Performance

We chose 3 finalists and invited them to present their ideas on stage at AT&T Spark in San Francisco last week. These are the innovators leading the way in AI development. See below for what they came up with.

Acumos AI Challenge Winner: Chris Buonocore

Chris took home $50,000 with his Acumos Property Assistant. This is a machine learning model for pricing your home based on fundamental property characteristics. Acumos Property Assistant presents an unbiased, transparent way to accurately price property and help simplify the complex process of selling a home. Congratulations, Chris!

Meet Chris and learn more about the Acumos property assistant in this video.

Acumos AI Challenge Finalist: Rhutvij Savant

Rhutvij claimed $25,000 for his Cancer Classification solution. The model, which classifies tumors as malignant or benign breast cancer, could provide more accurate prognosis and ultimately better care – potentially saving lives.

Acumos AI Challenge Finalists: Jinhe Shi and Yuhua Gong

Jinhe Shi and Yuhua Gong were awarded $25,000 for their User Review Prediction model. This solution is used to classify user reviews into positive and negative, giving business owners valuable insights to help improve service.

Not only did these finalists walk out with prize money and serious bragging rights, they also gained exposure and experience during AT&T Spark. AT&T’s invite-only innovation showcase offered the inventors a captive audience filled with industry leaders, fellow developers, and a host of reporters and analysts. Check out some highlights from the day for yourself in this video.

We want to thank all who entered the Acumos AI Challenge. We were impressed and inspired by the creative solutions we saw.

The challenge exemplified how open collaboration will help drive innovative solutions faster and more efficiently.

If you have an idea to bring to life, submit it to the Acumos AI project. Whether you’re a data scientist, developer, teacher or student – you can participate and influence the AI revolution.

This was originally posted at

Time is Running Out to Enter the Acumos AI Challenge

By Blog

We’re only 5 days away from the finish line of the Acumos AI Challenge!

The challenge, sponsored by AT&T and Tech Mahindra, is an open source developer competition for the Acumos AI platform seeking innovative artificial intelligence (AI) solutions. The companies, along with The Linux Foundation, encourage students, developers and data scientists to submit their ground-breaking AI solutions for a chance to win a share of $100,000 in prize money and industry recognition.

So far, more than 250 developers, students, and data scientists have registered for the challenge. The submission deadline is fast approaching, so we are encouraging all others who have a great idea for an AI solution to submit by 11:45pm US Pacific this Sunday, August 5.

The Challenge is looking for AI solutions across any and all use cases from 5G and SDN to enterprise solutions to media and entertainment. So, show us what you’ve got!

The top three teams will win a trip to participate in the finals at AT&T Spark in San Francisco on September 10, where the 1st place team will win the $50,000 grand prize. The 2nd and 3rd place teams will each be awarded $25,000. Plus, all finalists will have their solution featured in the Acumos Marketplace, receive PR exposure, and have the opportunity to meet with AT&T and Tech Mahindra executives during the event. In addition, all eligible AI solutions entered will be made available as open source in the Acumos Marketplace. For the rules of the Challenge, including who can participate, please see

A getting started guide is available to download, and any questions about the challenge can be directed to We look forward to checking out your solution!

The Next Wave of Digitalization is Simmering

By Blog

A short story by Nikhil Malhotra, Head of Maker’s Lab at Tech Mahindra


We are at the cusp of a new technological revolution powered by virtualization, SDN/NFV and 4G LTE. The action is being further amplified by 5G, which is set to redefine business across the globe. Thanks to this trend, the next wave of digitalization driven by artificial intelligence and machine learning is simmering in the future. This is where organizations are expected to face their next immediate challenge in trying to understand natural language and intent of customers using deep neural network techniques. We are still a far cry from making machines that can “understand” and deliver a response to fit our expectations. However, efforts are already underway taking us in the right direction.

The new environment is turning most traditional ways of doing things obsolete. At its peak, we expect many traditional operations to undergo a radical transformation with the application of speech recognition, natural language processing, artificial intelligence, analytics and machine learning. This brings us to some emerging new roles to facilitate machine learning (helping machines become intelligent) and analytics.

In this digital age, the enterprise is also experiencing a marked shift in the consumer purchase cycle. It is impacting the way marketers choose to manage media and publishing – a key aspect of promoting mass consumption. Given this trend, marketers are shifting their attention to on-going customer experiences. This heralds a critical shift in consumer trust from published media to peer opinion, which is what is driving service uptake and loyalty.

This has a major impact on how we view digital, leading to an altogether new way of creating content and services, and dramatically changing the traditional customer-supplier equation. It is increasingly driven by these evolving technologies that offer new lifestyle and insights

Also, it should be noted that the era of customers reaching out to the supplier is long gone. Now it is the supplier that must seek out the customer, and do so through peer influence, and not using conventional media.

Today, the word ‘convenience’ has assumed an all-important role in not just discovering new services but also in delivering and managing them. Product life-cycles have become shorter, placing a very high demand on traditional ways of creating and delivering products. More often than not, the very nature of time required to release a product makes it obsolete by the time it reaches the market.

This is where convenience can manifest through data-driven personalization. And, in this context, data means patterns that a machine would understand to first automate mundane processes to then drive business efficiency.


Digital information coupled with personalized perception, learning and even prescription is the future we are looking at. Today we are at a “scale 2” where organizations have realized the story and are pushing towards automation. The next level that we call as “scale 3” would see hyper-automation followed by “scale 4” where we see Cognition

Figure 1

Figure 1: This is the writer’s prediction based on the trends and experience.


The range of AI applications that we are looking from a commercialization standpoint include the following:

  • We are looking at fluid operations in call centers using natural language processing via machine learning and AI.
  • The onset of bots in how we do operations, be they rail ticketing, getting content on devices, getting directions to a restaurant, etc.
  • We are looking at an onset of robots in homes taking your EMRs and raising alarms when it concerns a citizen’s wellness.
  • We are looking at machines using adversarial neural networks to try to be better at a task, like coding, calculating costs and even project management.

As far as jobs are concerned, we are looking at a completely new class emerging, the “New collared jobs” and yes it involves re-skilling and to some extent dismantling the current job structure of frameworks. The traditional worker model will be disrupted in the coming 5 years. A new job role will emerge and that will be a “Neuro Linguistic Programmer” as I call it, a person who knows how to talk to machines and understand the vastness of information that comes out of these machines.


It is prudent to check what the actual reality is which is likely to be far from the truth. The figure below showcases the trends in 2017 as a report from McKinsey and Company:

While there have been fantastic investments from the big 5 in AI, the adoption has been very low. Most industry champions have been reluctant to apply AI and machine learning for customer-facing tasks, primarily because the mechanism to test the efficacy of output is a big question at this time. Some other trends that are visible in the market today are as follows:

  • There has been a lack of a unified framework for A.I. and a common ecosystem. The current trend of AI has been clustered with PhD students and scientists trying to improve the algorithm by a percentage value. There is a lack of a common ecosystem for developers to use AI algorithms. Developers today utilize different toolsets – (Caffe, Tensorflow, Python, Keras, etc.) to make machine learning models.
  • Commercial deployments of AI in areas of speech and image processing have primarily been from the big 5 software houses. Research on these techniques started some 3 decades ago but the usage in a production environment has been limited.
  • Industry suffers from a severe lack of AI innovation, particularly in communications and networking, thus prompting data and voice providers to look at old statistical automation techniques to achieve desired results. This has led to more rules-based systems than a pattern matching system which is being proposed.
  • Deployment of AI systems continues to be difficult with models while integration with different workflows still remains amongst the biggest challenges.


The above challenges prompted Tech Mahindra and AT&T’s research wings to look at the problem differently. The idea that emerged was to build a system that would serve to harmonize the dysfunctional world of AI imagine if developers across the world had a common platform to look at models, download these models, test them, share them back by updating and also stitch different models together; this was the thought that triggered the creation of ACUMOS.

When the two teams lingered on this thought, a set of guiding principles emerged. There were many to start with, but the teams settled on four such guiding principles or mantras to ensure that the world of AI creates the desired impact, post-ACUMOS. These principles are as follows:

  • Acumos would provide a common open source framework of AI. A framework where freelance developers and developers from various organizations could see practical models being built, shared and utilized. The goal of the framework is to have models that could be practically utilized in a production environment to attain business efficiency.
  • It would be a distributed marketplace with a federated structure. A world which is open to anyone to look at and a world which I wanted to keep closely knitted to a certain set of developers.
  • The framework would allow the creation of interoperable microservices as deployable units. With this, services could solve only one specific use case at one time and the services that would be easily utilized by others. This has a wider connotation. In today’s world, different developers are using different toolkits to generate models. Let’s explain it via an example. A developer manages to create a model to understand image category using convolutional neural network using a Tensor Flow toolkit. Another developer uses scikit learn to create models to understand sentiments on an image base. Now, Acumos considers these two pieces as two interoperable services. By a simple drag and drop, an organization or a developer can combine these two complex models (viz. model to find objects and model to recognize sentiments) to create a complex solution. This complex solution now becomes a solution for an ad agency to find out the efficacy of an advertisement placed for its users.
  • A framework that would expedite innovation.

A pictorial representation of tenets would appear as follows:

Figure 3: Acumos marketplace features


  • Ability to create and onboard models
  • Ability to download models, train with your own data sets
  • Share the models with a targeted group or the world
  • Ability to chain complex models
  • Execute in a targeted environment such as a Docker image

With the ACUMOS platform, we’re working to create an industry standard for making AI apps reusable and easily accessible to any developer. AI tools today can be difficult to use and often are designed for data scientists. The ACUMOS platform will be user-centric, with a focus on creating apps, microservices and an ability to stitch models to create complex services for a business.



Well, another question that is often asked is whether the world is doomed, and we are looking at another SKYNET?

I don’t think we are even near to something like this in the next 200 years. The chances for the human race to experience a crisis due to weapons is a more likely cause of destruction than AI components. The reason is very simple; what we have achieved from statistical models and machine learning /deep learning has still not gotten us to a very advanced AI. Also, AGI (artificial general intelligence) still remains a pipe dream and it involves many factors to make it a reality.

Today, machines are capable of doing certain tasks exceedingly well. Give it a pattern and data to crunch upon, and a machine will also classify and predict outcomes. This is nowhere close to how the human brain works. We are far cognitive and emotional beings who house our intelligence in a small portion of our brain called the ‘neocortex’. It is the neocortex which is involved in making cognitive decisions be it our faith, our emotional responses or our response to the society at large. In the future, what we build with machines will eventually get closer to this cognition. The reptilian part of the brain which is an appendage from the reptilian brain is not currently being positioned in AI.

Our harmonization via ACUMOS can see a breed of new algorithms emerge, and here we are talking about algorithms other than the neural networks that are our mainstay today.


A change is on the horizon, and that change looks ominous. We are looking at a world where humans will work together with machines in doing some of the mundane tasks we today perform. However, we need to embed that change in our psyche and work towards more AI + IA, that would enable us to intelligently augment machines. For this, we need a common ecosystem and harmony amongst developers, researchers and hobbyists alike to create a standard for the world to work with.




“We are reminded of the limitless-ness of human curiosity when we see man and machine create marvels for the future together,” is the quote Nikhil Malhotra lives by.

Nikhil Malhotra is the head of Maker’s Lab, a unique Thin-q-bator space within Tech Mahindra with over 17+ years of experience in a variety of technology domains.

In his present avatar, he is the head of Tech Mahindra’s R&D space called the Makers Lab which he created in 2014. The lab focuses on artificial intelligence, robotics and mixed reality. Nikhil’s area of personal research has been natural language processing, enabling machines to talk the way humans do. Nikhil has also designed an indigenous robot in his lab, as a personal assistant.

He lives by a dream of creating smart machines that would wed human emotions with artificial intelligence to make lives better.

He is also a leading speaker on digital transformation, practical use of AI and its future.

He holds a Master’s degree in computing with specialization in distributed computing from Royal Melbourne Institute of Technology, Melbourne.

Nikhil currently resides in Pune with his wife Shalini and son Angad.

Join Us at Acumos AI Day

By Blog

Join us in Paris on July 13 at the Open Source Acumos AI Day

Please join us on July 13 for the Open Source Acumos AI Day in Paris hosted by Orange and sponsored by Nokia. The Open Source Acumos AI Day in Paris is a seminar featuring business leaders and industry experts, with a strong focus on open source strategies for Artificial Intelligence, Machine Learning and Deep Learning.

The Open Source Acumos AI Day in Paris will showcase open source analytics and AI strategies from network operators and hardware providers. It will highlight the growing role of open source software in the development of an AI marketplace. Discussions will also profile the LF Deep Learning Foundation, the umbrella project hosted by The Linux Foundation to further open source collaboration in the AI, ML and DL space. The Acumos AI Project is an LF Deep Learning Foundation project.

Come to hear a great range of talks from experts at Orange, DT, Tech Mahindra, Nokia, Huawei, IBM, and the Linux Foundation. See the full schedule and register today by visiting the event page. There is no registration fee for this event.

We look forward to seeing you there!
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Abstract Background

What is Acumos?

By Blog

This post introduces the Acumos AI platform, an open source framework to simplify the development of artificial intelligence and machine learning applications.

Acumos wraps tools, such as TensorFlow and SciKit Learn, and models with a common API that allows them to seamlessly connect. It is language agnostic, supporting tools and models built in a variety of popular software languages. Acumos also is designed to leverage modern microservices. Anyone building an AI application on Acumos can export the libraries, models and all other required information as Docker files. Lastly, Acumos includes the Acumos Marketplace – a shopping portal from which to browse a catalog of AI models contributed by the community. To make Acumos incredibly easy to use, the platform also contains a visual editor with drag-and-drop functionality.

Overall, we hope that Acumos will enable a massive acceleration in AI adoption and innovation. Please read on!

# # #

By Reuben Klein, AT&T

Over the last several years, there has been tremendous excitement around and interest in the potential for machine learning (ML) technologies applied to artificial intelligence (AI). Tech giants and venture capitalists have invested tens of billions of dollars in building AI technologies. But we are still in the very early stages of AI development. Unfortunately, the talent to build AI remains scarce and expensive. An analysis by Tencent found that there are only 300,000 engineers in the world with the skills necessary for AI development. Compiling and cleaning the massive amounts of data required to make AI applications work is also expensive and time consuming. Constructing and maintaining all the infrastructure required for AI is also costly and time consuming.

In reality, most of the organizations building AI applications are using the same software and tools to create custom AI environments.  This is because the ecosystem lacks common, easy-to-use frameworks for developing AI applications. Contributors to the Acumos AI Project have felt this same pain. This same problem – lack of talent, expensive to build – prevents many organizations from exploring AI applications even though they have strong data science or analytics capabilities.

To address this gap and make AI accessible to a much wider group of people, including within our own organizations, we built the Acumos AI platform. An open source platform, Acumos simplifies and streamlines the training, integration, and deployment of AI models. Acumos provides a visual editor that allows AI microservices to be chained together to form a robust application with automatic API matching from each component to the next.

By creating an open source community around deep learning, artificial intelligence, and machine learning, The Linux Foundation hopes to accelerate the transition to AI-based software across a wide range of industrial and commercial problems. Uses cases we aim to address with Acumos span telecommunications and networking, advertising and media, academic research, and the Internet of Things, to name a few. Because it is so easy to onboard new tools and models into Acumos, there are a nearly infinite number of potential use cases.

A Platform for Builders, Creators, and Users

At its core, Acumos is designed to simplify and accelerate AI for three key groups. For builders of AI infrastructure, Acumos is designed to streamline setup and reduce the need to maintain complex build and deploy systems. For creators of AI models – data scientists and others – Acumos empowers them to publish models while shielding them from the need to custom develop fully integrated AI solutions. Acumos creates an extensible mechanism for packaging, sharing, licensing, and deploying AI models and publishes them in a secure catalog that is easily distributed between peer systems. It packages each model into an independent, containerized microservice, which is fully interoperable with any other Acumos microservice, regardless of whether it was built with TensorFlow, SciKit Learn, RCloud, H2O, or any other supported toolkit. Models built with any of these tools or any supported language, including Java, Python, and R, can be automatically onboarded, packaged, and cataloged.

For users of AI – the people and organizations using AI to add machine intelligence into their applications – Acumos microservices are easy to integrate into practical applications. A visual editor allows any software developer, even without a background in data science or knowledge of any specialized AI development tools, to construct simple or chained AI applications in Acumos and deploy them as containers to any suitable external environment that supports Docker.

A Community-Driven Marketplace for Models and Solutions

To tap the power of the AI community, we included the Acumos Marketplace, where members can share models and test data in a secure environment. The Acumos Marketplace contains mechanisms for data-powered decision making and artificial intelligence solutions. Self-organized peer groups across one company or across multiple domains can securely share information among themselves on how AI solutions perform, with ratings, popularity statistics, and user-provided reviews. The Acumos platform and Acumos Marketplace will aid communication between data scientists and application developers in order to automate the process of user feedback for selecting models while automating error reporting and software updates for models that have been acquired and deployed through the marketplace.

A Federated Model for AI Development and Deployment

After great deliberation, we decided to use a federated model for Acumos. Here’s how it works. Organizations wishing to deploy the Acumos platform can download the software and run it on the infrastructure of their choice. They can run it as a standalone node or they can join multiple Acumos nodes together. They can even choose to join Acumos nodes with other organizations or partners. For models and solutions published in their local Acumos Marketplace catalogs, they can elect to share them globally with all Acumos users, with federated groups of Acumos users or only with Acumos users on that node. The sharing granularity and security will allow groups to share data to solve problems more effectively. For example, a group of telecommunications and security companies may share network DDOS signature data or models used to detect DDOS to benefit the entire community.

Making AI Accessible to All

With Acumos, software developers can transition from code-writing and editing AI toolkits into a classroom-like code training process. AI models can be acquired from the marketplace, trained, graded on their ability to analyze datasets, and integrated, automatically, into completed solutions. They can access encapsulated AI models, without knowing the details of how they work, and connect them to a variety of data sources, using a range of data adaptation brokers, to build complex applications through a simple chaining process.

Acumos also includes a visual design tool called Design Studio for chaining together multiple models, data translation tools, filters, and output adapters into full end-to-end solutions that can be packaged and deployed into any run-time environment, such as a cloud service, including AIC, Azure, and AWS, a private datacenter, or a specialized hardware environment designed to accelerate AI applications. Acumos only requires a container management facility, like Docker, to deploy and execute portable general-purpose AI applications.

We hope that you will download Acumos, contribute models and tools, and join our community. Your participation will make AI accessible to all.