SambaNova CEO: Pre- to post-AI transition will be ‘bigger than the Internet’

SambaNova systems

Why are SambaNova and its competitors in the data center AI space attracting such huge amounts of funding? Is it required to get the product right, does it take this amount of resources to go up against the incumbents, or does it simply reflect investors’ view of the market opportunity?

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Conversational AI Platform partners Glia to empower Financial Institutions with meeting clients in Digital Environment

Active AI - Glia

Active.Ai, a conversational AI platform developed for financial services, and Glia, a provider of digital customer service, have teamed up in order to empower financial institutions with meeting their customers in the digital domain.

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Fei-Fei Li has been appointed to a federal task force on AI

Fei-Fei Li

The Office of Science and Technology Policy and the National Science Foundation have announced the newly formed National Artificial Intelligence Research Resource Task Force, which will write the road map for expanding access to critical resources and educational tools that will spur AI innovation and economic prosperity nationwide.

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Lack of AI implementation may have cost enterprises $4.26T, Signal AI finds

State of decisions 2021 infographic

Signal AI, which offers a decision augmentation platform infused with AI, interviewed 1,000 C-suite executives in the U.S. for the study. The report found 85% of respondents estimate upwards of $4.26 trillion in revenue is being lost because organizations lack access to AI technologies to make better decisions faster.

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[Paper] GraphiT: Encoding Graph Structure in Transformers

We show that viewing graphs as sets of node features and incorporating structural and positional information into a transformer architecture is able to outperform representations learned with classical graph neural networks (GNNs). Our model, GraphiT, encodes such information by (i) leveraging relative positional encoding strategies in self-attention scores based on positive definite kernels on graphs, and (ii) enumerating and encoding local sub-structures such as paths of short length.

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Training and Optimizing a 2D Pose Estimation Model with the NVIDIA Transfer Learning Toolkit, Part 2

The first post in this series covered how to train a 2D pose estimation model using an open-source COCO dataset with the BodyPoseNet app in the NVIDIA Transfer Learning Toolkit. In this post, you learn how to optimize the pose estimation model in the NVIDIA Transfer Learning Toolkit. It walks you through the steps of model pruning and INT8 quantization to optimize the model for inference.

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[Paper] IEEE Publishes comprehensive Survey of bottom-up and top-down Neural Processing System Design

In a new paper, a team from the IEEE (Institute of Electrical and Electronics Engineers) provides a comprehensive overview of the bottom-up and top-down design approaches toward neuromorphic intelligence, highlighting the different levels of granularity present in existing silicon implementations and assessing the benefits of the different circuit design styles of neural processing systems.

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Feature Selection – All you ever wanted to know

Feature engineering

Although your data set may contain a lot of information about many different features, selecting only the “best” of these to be considered by a machine learning model can mean the difference between a model that performs well–with better performance, higher accuracy, and more computational efficiency–and one that falls flat. The process of feature selection guides you toward working with only the data that may be the most meaningful, and to accomplish this, a variety of feature selection types, methodologies, and techniques exist for you to explore.

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Slintel scores $20M Series A as buyer intelligence tool gains traction

Globe infographic

One clear outcome of the pandemic was pushing more people to do their shopping online, and that was as true for B2B as it was for B2C. Knowing which of your B2B customers are most likely to convert puts any sales team ahead of the game. Slintel, a startup providing that kind of data, announced a $20 million Series A today.

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[e-Book] Intelligent Process Automation

Robotic Process Automation (RPA) is a technology gaining grounds in enterprises with a demonstrable business value. RPA helps businesses automate repetitive business processes to bring down TAT to minutes from hours and even days.

But what if you could train the software to self-learn and make intelligent decisions to cut human dependency as much as possible? Add Artificial Intelligence (AI) capability to turn automation into Intelligent Process Automation (IPA).

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Data flow automation engine Prefect raises $32M


Prefect, which was founded in 2018, offers a platform that can build, run, and monitor up to millions of data workflows and pipelines. The company’s hybrid execution model keeps code and data private while taking advantage of a managed orchestration service. Customers can use Prefect for scheduling, error handling, data serialization, and parameterization, leveraging a Python framework to combine tasks into workflows and then deploy and monitor their execution through a dashboard or API.

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OnSolve and Base Operations partner to provide Customers with contextual based, Risk Assessment capabilities powered by AI


The new partnership will enable customers to leverage a first-of-its-kind, AI-powered Risk Assessment driven by Onsolve’s AI and Machine Learning technology found on the Onsolve Platform for Critical Event Management and the Micro-Intelligence, Street-Level Threat Analysis From Base Operations.

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Osome raises $16M to automate repetitive accounting tasks


AI-powered accounting platform Osome today announced that it raised $16 million in a series A funding round. The company plans to put the proceeds, which bring its total raised to over $24 million, toward expanding its footprint internationally and building new product integrations.

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Is AI intelligent enough to solve Humanity’s problems?

Life finds a way

Every day at work I wrote Machine Learning algorithms, managed existing models, automated workflows and I was proud that not only is AI enabling me in performing better, but it is also increasing my organization’s productivity. To my realization, it was not AI, it was years of work that humans have done by using resources that occur in nature. Do not get me wrong, I believe in AI and do not dislike it, in fact, that’s my daily driver of work. I am only against its usage at present, where it is exploiting more resources than it is generating. My idea is to use AI responsibly and use it for the betterment of lives.

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