DeepBrainz: Our Mission is to Build, Advance & Democratize Tomorrow's State-of-the-art Artificial Intelligence for All to Access, and Use, for Empowering Humanity Universally.
- DeepBrainz AI BRAIN (DeepBrainz Research of AI Neurons), an AI R&D Team from DeepBrainz Technologies Private Limited, A DPIIT Recognized AI Company
DeepBrainz aka DeepBrainz Technologies, an AI Initiative & Technology Startup, Advancing, Building, and Democratizing the State-of-the-art (SOTA) AI & Cutting-edge Technologies, for "Empowering Humanity Universally" through Innovation, Research, and Product/Technology Development, Breakthroughs via "AI for ALL" Strategy
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:
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 via Custom AI Algorithms Development 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.
Our Proposed Solutions to the above-said problems are as follows:
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 DevOps.
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,
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:
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.
We are still working for an entirely new ecosystem and emerging business model.
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.
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.
Building Universal AI: State-of-the-art AI Developing AI with future Artificial #SuperIntelligence: which is Tomorrow’s State-of-the-art AI that builds/develops AI, through BRAIN (Brainz Research of AI Neurons), AI Research Team and AI BRAIN Dev, AI Development Team at @Deep-Brainz, “Empowering Humanity!”https://deep-brainz.github.io/Univers… For more information: Visit DeepBrainz Blog
DeepBrainz’s Universal AI is the strategic & game-changing master project with custom AI Algorithms Development, focuses on building the future DeepBrainz EthicalAGI (Auto-E-AGI) for Automated and Ethical Artificial General Intelligence and Ethical Superintelligence EthicalASI as Tomorrow’s Advanced State-of-the-art AI in the Universe.
We, DeepBrainz AI, will follow some top AI organizations in the industry such as Google AI (http://google.ai), DeepMind AI, OpenAI in many great ways for building tomorrow’s state-of-the-art AI! Thanks to everyone for such wonderful work on AI.
Arunkumar Venkataramanan, Founder & CEO at DeepBrainz Technologies Pvt Ltd  (DeepBrainz Intelligent Systems), Developing the state-of-the-art AI solutions through cutting edge technologies for the world’s biggest problems.
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DeepBrainz Technologies aka DeepBrainz, A DPIIT Recognized AI & Technology Startup Company, Bengaluru, Karnataka - 560079, India
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