Lambda raises $24.5M for AI-optimized hardware infrastructure

Lambda, an AI infrastructure company, this week announced it raised $15 million in a venture funding round from 1517, Gradient Ventures, Razer, Bloomberg Beta, Georges Harik, and others, plus a $9.5 million debt facility.  The $24.5 million investment brings the company’s total raised to $28.5 million, following an earlier $4 million seed tranche.

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IBM chief data scientist makes the case for building AI factories

AI factories

IBM distinguished engineer and chief data scientist John Thomas contends that for organizations to really embrace AI, they need to adopt a factory model that automates as much of the model building process as possible. Just like a traditional factory would create physical products reliably at scale and at speed, an AI factory would enable businesses to quickly build and scale trustworthy AI models.

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Credit Risk Modelling in the era of AI

Credit processing

The process of quantifying credit risk is an intricate task. It entails estimating factors such as Probability of Default (PD), the likelihood that the borrower will not be able to make the due payments; Exposure at Default (EAD), the expected value of the loan, or the value the lender is exposed to at the time of default; and Loss Given Default (LGD), the amount the lender loses at the time of default.

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ModelOps vs. MLOps to address Last-mile delivery challenges

Code on a laptop

Decisions organizations make today about building their AI pipelines will have major impacts on the future of AI maintenance for their organizations. But is everyone thinking about the future? The path taken today could impact the ability to add new functionality in the future or require starting from scratch. In order to build the right AI pipeline for your organization, you must identify the risk mix of tools to address the different parts of the pipeline, avoid vendor lock, and control costs.

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AI ethics champion Margaret Mitchell on self-regulation and ‘foresight’

Good and the bad

Ethics and artificial intelligence have become increasingly intertwined due to the pervasiveness of AI. But researchers, creators, corporations, and governments still face major challenges if they hope to address some of the more pressing concerns around AI’s impact on society. Much of this comes down to foresight — being able to adequately predict what problems a new AI product, feature, or technology could create down the line, rather than focusing purely on short-term benefits.

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The CI/CD Model development process

CI/CD model development

A software platform for organizations and developers to responsibly deploy, monitor, and get value from AI – at scale. Continuous Integration (CI) and Continuous Delivery (CD) are staples of a modern software development workflow that enable developers to release their code rapidly, reproducibly, and reliably. 

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Computer Vision software startup Algolux brings in $18.4M in Series B Funding

Algolux

Algolux, a computer vision startup that builds software for advanced driver assistance systems (ADAS) and for autonomous vehicles, has secured $18.4 million in new Series B funding from a group of investors that includes General Motors’ investment division, GM Ventures.
The new funding, which raises the Montreal, Canada-based company’s total funding to $36.8 million so far, was co-led by investors Forte Ventures and Drive Capital. Other investors include Investissement Quebec, Castor Ventures, Nikon-SBI Innovation Fund, GM Ventures, Generation Ventures and Intact Ventures.
The fresh influx of cash will be used by Algolux to help promote and grow the company’s computer vision and image optimization technologies with vehicle makers so they can use them with their future vehicles, according to the company. Algolux will also use the money to expand its engineering and marketing teams, while also exploring additional vertical markets for its technologies. The latest funding round was announced by Algolux on July 12 (Monday).
The company’s computer vision software is used with in-vehicle cameras as part of ADAS and autonomous vehicles in a market that is continuing to grow in use and popularity.
Image courtesy: Algolux
“Unfortunately, vision – the most widely deployed component of the overall perception stack – is still hampered by performance issues in low light and poor weather conditions making SAE Levels 2 and above more challenging to support,” the company said in its press release.
To battle this problem, Algolux uses computational imaging to design algorithms that treat the camera as part of the overall perception stack, which is a departure from the traditional siloed approach, according to the company. This approach resolves problems such as low light, low contrast and obstructions for object detection, imaging and geometric estimation, which provides clearer images and resolution. The use of the physical camera models also reduces training data needs by an order of magnitude, resulting in Algolux technologies outperforming commercial solutions by as much as 60 points in mean average precision (mAP), according to the company.
“We are thrilled to be taking this next step in the company’s trajectory and to do so with the trust and support of outstanding investors,” Allan Benchetrit, the CEO of Algolux, said in a statement. “Algolux is actively engaged with leading OEMs, Tier 1s, and Tier 2s globally. The consistent theme is a desire from customers to significantly improve the performance of their driving and parking vision systems in even the most challenging real-world situations.”
Shelly Kramer, analyst
Shelly Kramer, a founding partner and lead analyst with Futurum Research, told EnterpriseAI that Algolux’s latest funding news is an indicator of just how important computer vision is and how it will continue to move forward in the automotive sector.
“The fact that camera-based advanced driver assistance systems are table stakes when it comes to driving experiences today – both driver-led and autonomous – combined with the fact that camera tech still has a long way to go in terms of functionality and accuracy, means this is good news for the industry,” said Kramer. “Algolux’s computational imaging as part of the algorithm design process bodes well for all those days when my car’s camera tells me it can’t see because of weather conditions — and for the computer vision industry and the automotive industries. This is especially good news for the trucking industry and autonomous vehicles. This is an industry, and a company, to watch.”
James Kobielus, senior research director for data communications and management at TDWI, a data analytics consultancy, said the computer vision market today is “extraordinarily overcrowded” and that its use for automotive safety still has a long way to go before it is ready for primetime deployment.
James Kobielus, analyst
“I am impressed with Algolux’s focus on AI-powered cameras for robust perception in all conditions,” said Kobielus. “It approaches visual imaging as an integral, but not self-sufficient, component of the automotive perception stack. Without supplementary sensing inputs–such as radar, LiDAR, infrared, and ultrasound—and the composite AI to tie it all together in real time, automotive computer vision systems are extremely prone to mistakes from ever-present visual phenomena, such as low lighting, low contrast, and obstructed sightlines.”
The larger trend in the marketplace is the deployment of AI-driven perception stacks in which computer vision is essentially the sum of all sensor inputs that can be rendered as visual patterns, said Kobielus.
“Through sophisticated AI, it is increasingly possible to infer a highly accurate visual portrait from the radio frequency signals that people and objects reflect, the pressure and vibrations they generate, and the heat patterns that they radiate,” he said. “Algolux will need the funding to invest in the R&D necessary to improve its composite AI and to work with industry partners to build it into the ASICs necessary for ADAS safety applications.”

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Tags:
ADAS,AI,Algolux,artificial intelligence,autonomous driving,autonomous vehicles,computer vision,GM Ventures,IT investment,Machine Learning,self-driving cars,startup,venture capital

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10 business processes adapting Robotic Process Automation

Business process

Robotic process automation (RPA) refers to the automation of simple business processes. These are resolved through artificial intelligence (AI) and machine learning (ML) features on robots. Such processes are then defined as a list of repetitively done functions. Simply, automating your repetitive processes can produce quality output and efficiently use resources. Here are 10 business processes now using RPA technology.

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Aidoc raises $66 million in Series C round to address growing demand for its comprehensive AI Platform

Aidoc

Aidoc, a leading provider of artificial intelligence (AI) solutions for medical imaging, announced  a $66 million investment, bringing its total funding to $140 million. This Series C round, led by General Catalyst, follows a surge in demand for Aidoc’s AI-driven solutions, including the largest clinical deployment of AI in healthcare through its partnership with Radiology Partners.

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Cybereason raises $275M at Series F, adds Steven Mnuchin to board

Cybereason

Cybereason, a US-Israeli late-stage cybersecurity startup that provides extended detection and response (XDR) services, has secured $275 million in Series F funding. 
The investment was led by Liberty Strategic Capital, a venture capital fund recently founded by Steven Mnuchin, who served as U.S. Treasury Secretary under the Trump administration. As part of the deal, Mnuchin will join Cybereason’s board of directors, along with Liberty advisor Gen. Joseph Dunford, who was chairman of the Joint Chiefs of Staff under Trump until his retirement in 2019.
Lior Div, CEO and co-founder of Cybereason, tells TechCrunch that the startup’s decision to work with Liberty Strategy Capital came down to the firm’s “massive network” and the “understanding of the financial and government markets that Mnuchin and Gen. Joseph Dunford bring to our team.”
“For example, the executive order on cybersecurity put out by the Biden Administration recommends that endpoint detection and response solutions be deployed on all endpoints,” Dior added. “This accelerates the importance of solutions like ours in the public market, and Liberty Strategic Capital has the relationships to help accelerate our go-to-market strategy in the federal sector.”
This round, which will be used to fuel “hypergrowth driven by strong market demand,” follows $389 million in prior funding from SoftBank, CRV, Spark Capital, and Lockheed Martin. The company didn’t state at what valuation it raised the funds, but it is estimated to be in the region of $3 billion.
Cybereason’s recent growth, which saw it end 2020 at over $120 million in annual recurring revenue, has been largely driven by its AI-powered platform. Unlike traditional alert-centric models, Cybereason’s Defense Platform is operation-centric, which means it exposes and remediates entire malicious operations. The service details the full attack story from root cause to impacted users and devices, which the company claims significantly reduces the time taken to investigate and recover from an enterprise-wide cyber attack. 
The company, whose competitors include the likes of BlackBerry-owned Cylance and CrowdStrike, also this week expanded its channel presence with the launch of its so-called Defenders League, a global program that enables channel partners to use its technology and services to help their customers prevent and recover from cyberattacks. Cybereason claims its technology has helped protect customers from the likes of the recent SolarWinds supply-chain attack and other high-profile ransomware attacks launched by DarkSide, REvil, and Conti groups. 
Today’s $275 million funding round is likely to be Cybereason’s last before it goes public. Div previously said in August 2019 the company planned to IPO within two years, though he wouldn’t be pressed on whether the company is gearing up to go public when asked by TechCrunch. However, the company did compare its latest investment to SentinelOne‘s November 2020 Series F round, which was secured just months before it filed for a $100 million IPO.

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Cape Analytics raises $44M to automate property inspections with AI

Cape Analytics

Cape Analytics, a company meshing AI with aerial imagery to power property insurance inspections, has raised $44 million in a series C round of funding. Founded in 2014, Mountain View, California-based Cape Analytics enables insurance providers such as AXA and Western Mutual to optimize their underwriting process by leveraging computer vision and geospatial imagery to evaluate properties without having to send someone out to physically inspect it.

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Simpplr raises $32M for its intranet platform

Simpplr

Simpplr, a modern platform for building intranet sites (or ’employee communications and enablement platforms,’ as the company calls it), today announced that it has raised a $32 million Series C round led by Tola Capital. Norwest Ventures, which led the company’s Series B round last year, as well as Salesforce Ventures and George Still Ventures also participated. This brings Simpplr’s total funding to just over $61 million.

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Geometric foundations of Deep Learning

In October 1872, the philosophy faculty of a small university in the Bavarian city of Erlangen appointed a new young professor. As customary, he was requested to deliver an inaugural research program, which he published under the somewhat long and boring title Vergleichende Betrachtungen über neuere geometrische Forschungen (“A comparative review of recent researches in geometry”).

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The IMPACT 50 List for Q3 2021

The Impact list

Quarterly IMPACT 50 List of the most important movers and shakers in our industry. These companies have proven their relevance by the way they’re impacting the enterprise through leading edge products and services. We’re happy to publish this evolving list of the industry’s most impactful companies!

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Top 10 tools to kickstart your MLOps journey

Pipelines

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 Grid.ai 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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Top 10 Artificial Intelligence Mergers and Acquisitions in 2021

Mergers & Acquisitions

The sudden outbreak of the coronavirus pandemic has created a drastic effect on all types of businesses since March 2020. There are several Artificial Intelligence mergers and acquisitions that happened or yet to happen within hi-tech companies. The tech giants are always ready to acquire small companies that can help in earning better revenue and customer engagement with new products and services in the global market. This provides a competitive edge for the hi-tech companies in the tech-driven market across the world. Let’s explore the top 10 Artificial Intelligence mergers and acquisitions in 2021 for the welfare of society.

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London VC The Untitled Ventures raises €100M to invest in Eastern European tech startups

London-based early-growth VC, The Untitled Ventures has raised €100 million (around £85M) for their latest fund, which will focus on Eastern European breakthrough tech startups — especially those looking to relocate to Western Europe and the UK. The fund aims to help promising startups, that might have innovative and world-changing products but struggle to get investment because of their location. 

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