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By Arunkumar Venkataramanan

DeepBrainz AI Inception Story – How we began

18 November 2019 AI

DeepBrainz AI (DeepBrainz Technologies)

DeepBrainz’s Mission, Vision, and Core Values

Mission: “Advancing, Building, and Democratizing Tomorrow’s State-of-the-art AI for ALL to Access, and Use, for Empowering Humanity Universally”

Vision: “AI for ALL is the key to Empowering Humanity Universally”

Core Values: “Passion, Purpose, Determination, Intellectual Curiosity, Innovation, Integrity, Inclusive Diversity, Impact Focus, Creativity of Fun & Cool Ideas, Emergent Leadership, Problem Solving, Deliberate Practice, Effectiveness & Efficiency, Strategic Focus & Execution, Commitment, Disciplined Growth, Simplicity from Complexity, Diligence, Accountability (Responsibilities), Communication, Excellence & Expertise with Skills & Competencies, Thinking Bigger, Starting Simpler, Moving Faster, Working Smarter, Being Grittier with Growth Mindset (Learnability), Making a Difference”

And, our core values are not subject to change but may evolve!

DeepBrainz, A DPIIT Recognized AI Company

DeepBrainz AI aka DeepBrainz Technologies (DeepBrainz Intelligent Systems),  A DPIIT (DIPP) Recognized AI & Technology Startup Company, A Universal AI Platform for Enterprises, End-Users, Developers, Researchers & Everyone, A Google Cloud Partner, An AWS Partner Network (APN) Technology Partner, A Microsoft Partner Network MPN Member, An IBM PartnerWorld Member, An Adobe Exchange Partner Member, A SAP PartnerEdge – Build (PE Build) Partner Member, A Cloudera Connect Partner Member, A Confluent Partner Member, A Nutanix Partner Network Authorized Partner Member, A Salesforce AppExchange Partner, A Red Hat Connect for Technology Partner, A VMware Partner Enrolled Member, An Oracle Partner Network OPN Enrolled Member (also a part of Oracle for Startups), An NVIDIA Inception Program Member, A Neo4j Startup Program Member, A Member of DigitalOcean Hatch, A Global Startup Program), An Udacity Talent Hiring Partner: Building, Advancing, Democratizing Tomorrow’s State-of-the-art AI for All and Cutting-edge Technologies, for “Empowering HUMANITY Universally” via “AI for ALL” Strategic Policy, Mission, Vision, Core Values.

DeepBrainz’s Philosophies and Principles are yet to be published.

We as DeepBrainz, through AI R&D, Innovation, and Breakthroughs, develop DeepBrainz’s Products such as Universal AI, via Custom AI Algorithms Development, that will be the strategic & game-changing, high-level AI Pilot Project with OKRs, focuses on Building and Advancing Tomorrow’s State-of-the-Art AI for future DeepBrainz Ethical AGI (Auto-E-AGI) i.e. Automated & Ethical Artificial General Intelligence.

DeepBrainz Universal AI, is a product as a Software, Platform, Infrastructure that offers the services of Automated Large-Scale Machine Learning & Deep Learning for Computer Vision, NLP, Robotics with Ethical Policies, Open & Authentic Large-Scale Datasets with ML/Data Infrastructure to achieve towards the future Ethical Superintelligence EthicalASI as Tomorrow’s Advanced State-of-the-art AI in the AI Universe.

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DeepBrainz’s Strategy

We as DeepBrainz, with various strategies to deliver the highly unique solutions as Products focusing Innovation to be DeepBrainz as a Future Revolutionary AI & Technology Startup Company, Aims Influencing Industries, Democratizing Tomorrow’s State-of-the-art AI, & the Cutting-edge Technologies, for “Empowering Humanity Universally!” via the Strategic Policy called as

“AI for ALL is our best strategic policy to Empowering Humanity Universally” 

We as DeepBrainz, do plan to solve the biggest and the Large-Scale real-world & business problems across industries especially in Healthcare, Education, Autonomous Systems and then for everyone, using Computer Science and Artificial Intelligence particularly now in the areas of Machine Learning, Deep Learning, Computer Vision and Natural Language Processing and then with Robotics towards future AGI (future General AI)

We have strategic plans to achieve and thrive in the fields with the help of Large-scale Evolving Business Models, State-of-the-Art AI with Cutting-edge Technologies, Business Operations, and the team with the great Talent who have a plan for the launch of our Products, Services as Platforms, Infrastructures via State-of-the-Art R&D at right Time by Building Auto ML/Data Infrastructure for In-House AI Capability that provides AI-as-a-Service through Open, Authentic, Large-Scale or Massive Datasets as Unified Data Warehouse with Strategic Data Acquisition from AI/Data Strategies, “Empowering Humanity Universally” via “AI for ALL” Strategic Policy.

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Problems that DeepBrainz primarily Focus to Solve

The problems that DeepBrainz primarily focuses on solving are in/from the field of AI and Machine Learning. We address the problems arise from the various challenges of Deep Learning, which is the emerging and popular sub-field of Machine Learning, are as follows:

  1. The Challenges in Labeling of training datasets which is crucial for Supervised learning,
  2. The Difficulties of creating and obtaining such massive datasets that are sufficiently large and comprehensive.
  3. The Difficulty of explaining in human terms results from AI models i.e. Explicability or Interpretability of the large and complex (deep learning) models.
  4. The Generalizability of the learning i.e. Generalization of the AI models continues to have difficulties in carrying their experiences from one set of circumstances to another.
  5. The Risk of the Bias in Data and Algorithms that causes the unintended bias and the security threats in some important use cases, for instance, in Healthcare and Cybersecurity that are concerned more social in nature.

While being solved the above-said problems for advancing Tomorrow’s State-of-the-Art AI, DeepBrainz also aims to focus on Building an In-House AI Capability and Leveraging AIaaS (AI-as-a-Service) for Our Own AI-based Customized Product for Innovation that is being built to the Enterprises and Consumers to further address the problems from the various major important & potential use cases across the industries’ sectors, and functions especially High Tech, Healthcare, Automotive, Education, Agriculture, Retail through Cutting-edge Technologies such as Machine Learning for democratizing AI Technologies.

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Our Proposed Solutions to the Problems

Our Proposed Solutions to the above-said problems are as follows:

  1. Promising new emerging techniques, for addressing the data labeling problem, such as  Reinforcement Learning (Deep RL), In-Stream Supervision, Unsupervised Learning.
  2. Developing a strategy to acquire massive datasets via say Data Strategy such as Strategic Data Acquisition, and Unified Data Warehouse.
  3. Achieving Explicability of AI Models through DeepBrainz AI Research for the explainability and interpretability of the AI/Deep Learning models.
  4. Developing the capability of such AI models using “Transfer Learning” to transfer knowledge to another domain. (Transfer learning—in which an AI model is trained to accomplish a certain task and then quickly applies that learning to a similar but distinct activity)
  5. Solving Bias via DeepBrainz AI Research to end the Risk of Bias in Data & Algorithms by understanding how the processes used to collect training data can influence the behavior of models they are used to train.

As organizations across the world planning to adopt significant deep learning efforts, We begin to build a complete in-house AI capability and AI-as-a-service for DeepBrainz and Everyone.

As per the use cases planned to build, we create a data plan that produces results and predictions, which can be fed either into designed interfaces for humans to act on or into transaction systems.

Our key data engineering process includes data creation or acquisition, defining data ontology, and building appropriate data “pipes.”

We do plan to develop robust data maintenance and governance processes and implement modern software disciplines such as Agile and AnalyticsOps (Analytics + DevOps).

And, the Uniqueness of DeepBrainz Proposed Solution is to further help deliver the Solutions to Various Several Other Problems around the world.

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Addressing the Advanced Problems

When it comes to scaling, DeepBrainz will work on to overcome the “last mile” problem of making sure the superior insights provided by AI are instantiated in the behavior of the people and processes of an enterprise and a user.

We’re working to build much of the construction and optimization of deep neural networks that remains something of an art requiring our real expertise to deliver step-change performance.

We notice that with AI techniques and data available, where the value is clearly proven, but the cost and complexity of deploying AI are still daunting.

Also with societal concerns and regulations, Regulatory constraints are especially prevalent in use cases related to personally identifiable information.

The use and commercialization of individual data on online platforms, the use, and storage of personal information are especially sensitive in sectors such as banking, health care, and pharmaceutical and medical products, as well as in the public and social sectors.

And so we’re planning to work on to justify the cost and issues around privacy and personal identification.

And, we plan to address the cases, where the value is not yet clear and the most unpredictable scenario is where either the data (both the types and volume) or the techniques are simply too new and untested.

As we will address these issues, businesses and other users of data for AI will need to continue to evolve our business model related to data use in order to address societies’ concerns.

Furthermore, regulatory requirements and restrictions can differ in accordance with the countries and the sectors.

We also often consider redefining the followings whenever possible,

  1. Formulating the Data and AI use cases, based on business priorities;
  2. Understanding the current state of our Data and AI projects and enablers;
  3. Defining the Data and AI vision and the execution roadmap, including investments;
  4. Executing the first use cases aiming at production readiness;
  5. Scaling up operations.

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Our Business Models

While coming to Marketing Strategies such as B2B, B2C, and B2D which are being utilized in accordance with the use-cases DeepBrainz will aim to solve for the enterprises, the consumer, and the developers respectively.

The business revenue models evolve and change over the use of data for AI. As AI Landscape can be divided into two segments in the following:

  • Infrastructure: We’re planning to run in the back-end and provide computational services to others. The business model we follow is generally based on API calls.
  • Application: These can be in the B2B and B2C space. Significant activity, however, is seen in the B2B space where we also plan to offer SaaS-based subscription services. Some of them may end up being purchased by important firms after several proofs-of-concept.

We additionally do plan for the development of a tailored-made solution and then make you pay monthly running costs as well as operational support/training, as we are also an AI development team specialized in building tailored-made solutions for clients.

We do plan to provide the data necessary to build the PoC.

It’s the known fact, “The more people using AI, the faster it learns.”

AI solutions are priced by transaction or completed computation. You’ll be required to pay as much as you use AI. i.e “Pay-as-you-go” method.

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As an AI Technology Provider and Applier

We, DeepBrainz will be the provider of AI technology, applier of AI technology, and policymaker, who sets the context for both.

As an AI technology provider company, We plan to develop or provide AI to others, since we have considerable strength in the technology itself with the data scientists, needed to make it work, with a deep understanding of end markets. Understanding the value potential of AI across sectors and functions can help shape the portfolios of our AI technology company. That said, we won’t necessarily only prioritize the areas of highest potential value. Instead, we will combine that data with complementary analyses of the competitor landscape, of our own existing strengths, sector or function knowledge, and customer relationships, to shape our investment portfolios. On the technical side, the mapping of problem types and techniques to sectors and functions of potential value can guide us with specific areas of expertise on where to focus.

We further plan to create a prioritized portfolio of initiatives across the enterprise, including AI and the wider analytic and digital techniques available. We are to create an appropriate portfolio, as it is important to develop an understanding of which use cases and domains have the potential to drive the most value, as well as which AI and other analytical techniques will need to be deployed to capture that value.

And, our portfolio ought to be informed not only by where the theoretical value can be captured but by the question of how the techniques can be deployed at scale across the enterprise and the question of how analytical techniques are scaling is driven less by the techniques themselves and more by our skills, capabilities, and data.

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As an AI Policymaker

We, DeepBrainz will need to consider efforts on the “first mile,” that is, how to acquire and organize data and efforts, as well as on the “last mile,” or how to integrate the output of AI models into workflows ranging from clinical trial managers and sales force managers to procurement officers. So, We plan to invest heavily in these first- and last-mile efforts.

As a Policymaker, we’ll need to strike a balance between supporting the development of AI technologies and managing any risks from bad actors. we have an interest in supporting broad adoption since AI can lead to higher labor productivity, economic growth, and societal prosperity. Our tools will include public investments in research and development as well as support for a variety of training programs, which can help nurture AI talent.

We believe that will work on the issue of data, as authorities can spur the development of training data directly through open data initiatives, and opening up public-sector data can spur private-sector innovation, Setting common data standards can also help. We are aware that AI is also raising new questions for policymakers to grapple with for which historical tools and frameworks may not be adequate. Therefore, some policy innovations will likely be needed to cope with these rapidly evolving technologies. But given the scale of the beneficial impact on business the economy and society, our goal will not be to constrain the adoption and application of AI, but rather to encourage its beneficial and safe use.
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We are working for an entirely new ecosystem and an emerging business model.

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Credit: We thank McKinsey and Gartner for providing the great insights that made us be more informed. We thank all our stakeholders for your interest in DeepBrainz AI.

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