Tech behind Water Resource Management startup Cranberry Analytics

Cranberry Analytics

In a conversation with Analytics India Magazine, Co-founder and CTO Shishir explained the technology behind Cranberry Analytics’ management system Recon, breaking down how AI and ML can facilitate the management of natural resources and what the future for AI in water management looks like. 

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Finally, Tech Giants are turning down Unethical AI Projects

Tech giants

The pros of artificial intelligence technology have always been followed up with the cons of leveraging these technologies. With the launch of every new system that requires users to share personal and biometric data, a school of thought has emerged voicing the ethical and privacy concerns of using the systems. A recent investigative report by Reuters sheds light on how the three US tech giants — Google, IBM and Microsoft — have been resisting and turning down projects on account of ethics concerns. 

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Top 10 Research papers on Federated Learning

In 2017, Google, in a blog post, ‘Federated Learning: Collaborative Machine Learning without Centralized Training Data,’ explained in detail the nuances of this technique. Since then, a lot has changed. In this article, we will list some of the top research papers on federated learning.

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USA’s National Science Foundation announces 11 AI-research Institutes across the Country

National Science Foundation

With an aim to bolster AI research across major American research institutes, the National Science Foundation (NSF) announced that it will establish 11 new National AI-Research Institutes. Each NSF institute has been awarded $20 million for the research that will go on for five years, to study the range of advances through AI-based technologies. The US Department of Agriculture National Institute of Food and Agriculture, and the US Department of Homeland Security will also work in collaboration with the NSF in the research.
NSF aims to empower America’s workforce with the knowledge of AI-tech, by deepening its research. Last year in August, NSF had announced the formation of seven national AI institutes. These institutes are aimed at AI research that would expand to a broader range of businesses across American economy. For such extensive AI research, NSF initially supported these institutes with an investment of $100 million. The establishment of these institutes represents America’s most significant federal investment in AI research and workforce development. Register for our Workshop > >
Objectives of 11 NSF AI-research institutes
NSF Director Sethuraman Panchanathan had last year said that NSF invests over $500 million in AI research annually. However, 2021 has been a year of more such establishments. This year, the combined investment of NSF in national AI-research institutes is $220 million, while the reach of all 11 AI-research institutes announced recently, will now expand to a total of 50 states in the US. 
Panchanathan described NSF’s vision in AI research to be an inspiration for talents and ideas across the country, as he said, “The field of AI-research is an important area which will lead the US to new capabilities in terms of improving lives from medicine to entertainment, transportation to cybersecurity. Such research initiatives will position the US in the vanguard of competitiveness.”
The eleven national AI-research institutes will focus on seven key areas:
See Also

AI and advanced cyberinfrastructure,AI for advances in optimisation,AI in computer and network systems,AI augmented learning,AI driven innovation in agriculture, food system,AI in dynamic systems, andHuman-AI interaction and collaboration
These AI-research institutions are being established by NSF in partnership with the US Department of Homeland Security, the US Department of Food and Agriculture, Google, Amazon, Accenture, and Intel. Most of the tech companies involved in the investment are already part of the business transformation that industries have witnessed using AI, worldwide. The NSF institutes will serve as the platform for broader studies of the transformational advances that can be achieved in various sectors— specifically in the fields of science and engineering, followed by the economic sector, cybersecurity, and the agriculture sector with food-system security.
Here’s a list of the 11 institutes, and their areas of focus in AI-research:
The research program led by Georgia Institute of Technology, also known as AI-CARING, will focus on AI systems that learn individual models of human behaviour. How the AI behaviour changes with time and uses the knowledge to collaborate and communicate in any given caregiving setting. This is NSF’s ‘AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups‘ which will be partially funded by Google and Amazon.In order to support the underrepresented students and teachers to learn AI-tech, NSF’s ‘AI Institute for Advances in Optimisation‘ will be led by Georgia Tech, to revolutionise decision-making on a large scale, by fusing AI and maths, into the intelligent systems— to achieve breakthroughs. The research will benefit high school, graduate and postgraduate students and the institute will be partially funded by Intel.The University of California San Diego will collaborate with five other universities across America, to “make impossible optimisations possible” by solving the challenges of scale and complexity. To achieve this, NSF’s ‘AI Institute for Learning-Enabled Optimization at Scale‘ will apply learning-enabled optimisation. The institute will be partially funded by Intel.With focus on AI in cybersecurity, NSF’s ‘AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment‘ will create cyberinfrastructure to make AI easy for scientists, to promote its democratisation in the country. This institute will be led by Ohio State University and will be fully funded by NSF.NSF’s ‘AI Institute for Future Edge Networks and Distributed Intelligence‘ will develop new AI tools and techniques to ensure that future generations of wireless edge networks are self-healing and self-optimised. The institute will design such wireless edge networks by leveraging the synergies between networking and AI. It will be partially funded by DHS.NSF’s ‘AI Institute for Edge Computing Leveraging Next-generation Networks‘ will work in collaboration with scientists, engineers, statisticians, legal scholars and psychologists from seven varisities in the country— to develop edge computing with landmark AI functionality, while controlling the costs. This institute will be partially funded by DHS, and will be led by Duke University.With focus on fundamental AI and machine learning, NSF’s ‘AI Institute for Dynamic Systems‘ will perform innovative research— to integrate models based on physics with AI and machine learning approaches. Led by University of Washington, this institute   will be partially funded by DHSNSF’s ‘AI Institute for Engaged Learning‘ will engage learners in AI-driven narrative-centered learning environments, through advanced natural language, machine learning and computer vision. The research is aimed at achieving transformational advances in STEM teaching also. It will be led by North Carolina State University and will be fully funded by NSF.Led by Georgia Research Alliance, NSF’s ‘AI Institute for Adult Learning and Online Education‘ will focus on adult online education by developing novel AI theories and techniques. This institute will be partially funded by Accenture.With focus on AI in agriculture, the ‘USDA-NIFA Institute for Agricultural AI for Transforming Workforce and Decision Support‘ will integrate AI methodologies into agriculture operations to better predictions and decision mechanisms, and robotics-enabled agriculture. It will be funded by USDA-NIFA.With an aim to transform the agriculture sector, the ‘AI Institute for Resilient Agriculture‘ will use innovative AI-driven digital twins that model plants at an unprecedented scale. This method is enabled by computational theory, tools for crop improvement and production for resiliency to climate change, along with AI algorithms. Led by Iowa State University, this institute will be funded by USDA-NIFA.
Recently, the national science foundation also issued a grant to the University of Florida, for a five-year study to find out ‘the public’s awareness of artificial intelligence’.
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Explained: NVIDIA’s record-setting performance on MLPerf v1.0 training benchmarks

Record setting

Last June, MLCommons, an open engineering consortium, released new results for MLPerf Training v1.0, the organisation’s machine learning training performance benchmark suite. The latest version includes vision, language and recommender systems, and reinforcement learning tasks. 

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10 best alternatives to OpenAI Triton

Last month, OpenAI released Triton 1.0, an open-source Python-like programming language that enables researchers to write highly efficient graphics processing unit (GPU) code. OpenAI claims Triton delivers substantial ease-of-use benefits over coding in CUDA, a programming tool developed by NVIDIA. The development repository for the Triton language and compiler is available on GitHub. 

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[Video] Accenture thought leaders discuss future of Data Science at Applied Intelligence Week

Rendezvous with AI

Applied intelligence lead at Accenture India Sanjay Sharma moderated the ‘future of data science’ forum. The panellists, including Rajamani Sambasivam, Fernando Lucini, Chandrasekharan Rajendra, and Tahney Keith, spoke about hiring data scientists, remote collaborations, demand and supply gaps and more. 

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Europe’s proposed AI law could cost its economy $36 bn

EU AI law

The European Commission, the executive arm of the EU, has proposed a new law to make Europe the global hub of trustworthy artificial intelligence (AI) called the Artificial Intelligence Act. It was proposed to guarantee security and fundamental rights of individuals and enterprises while strengthening wide AI takeover, investment and innovation.

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IBM launches AI Starter Kit to deploy ML Models faster (with Video)


IBM recently launched a new machine learning, end-to-end pipeline starter kit to help developers and data scientists to build machine learning applications and deploy them quickly in a cloud-native environment. The starter kit is part of the IBM Cloud-Native Toolkit–an open-source collection of assets that provide an environment for developing cloud-native applications for deployment within Kubernetes and Red Hat OpenShift. Assets created with the Cloud-Native Toolkit can be deployed in any cloud or hybrid cloud environment. 

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Are Google’s new Diffusion Models better than GANs?

Google AI has introduced two connected approaches to enhance the image synthesis quality for diffusion models: Super-Resolution via Repeated Refinements (SR3) and a model for class-conditioned synthesis, called Cascaded Diffusion Models (CDM).

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Top 10 tools to kickstart your MLOps journey


The MLOps market is expected to grow by almost $4 billion by 2025, according to analytics firm Cognilytica. Amazon, Google, Microsoft, IBM, H2O, Domino, DataRobot and have all incorporated MLOPs capabilities into their platforms. In this article, we list the best open-source MLOps tools and services to help businesses and individuals kickstart their MLOps journey.

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Garbage in, Garbage out: The problem of Data Labeling

Data labelling

In 2018, Amazon built a machine learning (ML) algorithm for hiring new candidates. However, because of substantial gender bias, it was scrapped soon. The fault did not lay in the algorithm but in the data that it was trained on. The ML algorithm was trained on Amazon’s previous hiring data. However, since the tech giant did not have a substantial ratio of men and women in jobs, the algorithm became biased towards men. Systematic bias was rationalised and reinforced through ML.

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Does MLOps live upto the Hype?

AI Gurus

Machine Learning (ML) model metrics are designed to monitor performance. But when a model goes into production, many factors influence its performance. The traditional checkpoints may no longer help as organisations look to scale these models (think: scaling from a million to billion credit card users). This is why experts advocate for MLOps, a branch of ML that brings together all the nice things from DevOps and ML. 

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How Coca-Cola and PepsiCo use AI to bubble up Innovation

Pepsi & Coca Cola

In its first year of operations, Coca-Cola sold only around 25 bottles of Coke. Today, the company sells approximately 1.9 billion servings of its drinks across over 200 countries. As many as 90 percent of the global population recognises Coca-Cola’s iconic red and white logo. In fact, despite sharing a smaller share of the market, its competitor PepsiCo is still one of the largest companies worldwide. 

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Dell releases Omnia to manage AI & HPC workloads

People on whiteboard

Dell has released Omnia, an open-source software package to simplify AI and compute-intensive workload deployment and management. Omnia automates the management of high-performance computing, AI, and data analytics to create a repository of hardware resources. Omnia has been developed by Dell’s HPC and AI Innovation Lab along with Intel and Arizona State University (ASU).

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Making a case for Serverless Machine Learning

The scale and complexity of machine learning make it hard to provide and manage data and resources efficiently. This hinders and decreases productivity. The easiest way to approach the problem is serverless machine learning. It is an excellent solution to the problem of data center resource management. Machine learning users face several daunting challenges that have a significant impact on their productivity and efficiency.

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What the Floq: The secret mission Of Google


Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs. 

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Hugging Face gets an Amazon treatment

AWS Hugging Face

To amplify the process of embracing natural language processing models via the power of AI, Amazon Web Service (AWS) chose to collaborate with Hugging Face, who are pursuing various ways to make this process elementary.

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[Paper] Causal Representation is now getting its due importance in Machine Learning

The research paper titled “Towards Causal Representation Learning” provides the way through which the artificial intelligent systems can learn causal representations and how the absence of the same in machine learning algorithms and models is giving rise to challenges in front of us. 

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