Every organization now relies on technology to remain competitive. Downtime is more expensive than ever and the volume and variety of machine data generated as logs, metrics, and traces means the old way of managing availability and performance is no longer an option.
InsightFinder, founded by Dr. Helen Gu, a system machine learning expert, helps companies like Dell, Credit Suisse, and China Mobile to detect system anomalies without thresholds, predicting severe incidents hours before they happen, and automatically pinpointing root causes. Customers report four to six hours of downtime reduction per incident and tens of thousands of dollars in cost savings monthly.
To support increased market demand, the company today announced a two million dollar investment from leading venture capital group Fellows.Fund, alongside the founder of a $100B+ software company, Silicon Valley Future Capital, Eastlink Capital, Brightway Future Capital and more than a dozen executives at Facebook, Uber, Pinterest, Amazon, and Airbnb. According to Alex Ren, Fellows.Fund Managing Director, “InsightFinder’s groundbreaking approach was vetted by the experts in the field, and the Fellows’ network and connections are poised to foster the company’s success.”
The company also announced it recently closed a deal with NTT DATA, the Japanese multinational information technology service company headquartered in Tokyo. NTT DATA will use technology from InsightFinder to help its customers reduce downtime for critical systems. Additionally, InsightFinder extended its existing partnership with Apprendis, makers of the Inq-ITS e-learning platform.
InsightFinder is also expanding its leadership team to include new Chief Revenue Officer, John Whittington. John has over 25 years of enterprise software sales experience. He co-founded BlueStripe Software and grew the business to a successful exit to Microsoft. According to John, “I’ve helped introduce monitoring technology to enterprise customers for nearly three decades and have never seen a stronger product than InsightFinder or a market more poised for disruption.”
5 Reasons To Leverage AI in Metal Fabrication
Emily Newton is the Editor-in-Chief of Revolutionized, an online magazine showing how technology is disrupting many industries.
There’s growing interest in using artificial intelligence (AI) in manufacturing to achieve numerous benefits ranging from better worker productivity to less machine downtime. Here are five specific advantages that AI can bring to metal fabrication. They’ll show you what’s possible.
Laser cutting is a popular and reliable metal fabrication technique. Implementing manufacturing automation while using it could lead to even better outcomes. Many laser cutters on the market work with metals and numerous other materials. They typically include cameras as identification measures. It’s essential that the laser cutter correctly recognize the material. A mistake could create messes or even release hazardous chemicals.
AI and computer science professionals at MIT recently developed the SensiCut system. It identifies 30 materials with deep learning and an optical method that examines a material’s microstructure with a laser. It can also suggest cutting adjustments or handle surfaces containing multiple materials.
Mustafa Doga Dogan, a doctoral candidate working on the project, said, “By augmenting standard laser cutters with lensless image sensors, we can easily identify visually similar materials commonly found in workshops and reduce overall waste.”
People interested in applying AI to get this benefit should first take the time to see which practices or materials typically cause the most waste. That information can guide the next steps concerning how and when to use AI to cut down on waste.
Unexpected equipment failures can become costly problems for metal fabricators and other industrial factories. That’s a primary reason why more companies use AI in manufacturing with the goal of cutting down those outages.
If leaders get notifications of impending equipment failures soon enough, they can adjust workflows, order parts or take other proactive steps to stop equipment problems from causing shutdowns. Moving ahead with AI-based maintenance gets factories closer to the zero-downtime goal. It can also aid decision-makers in choosing when to replace aging machines and see the best return on investment.
A company’s budget may limit its ability to invest in AI for the improved maintenance of all equipment. In such cases, the ideal approach is to determine which machines fail most often or are out of service for the longest periods. That information gives a good starting point when selecting where AI would get the best results when minimizing maintenance.
AI is also useful for helping metal fabricators deal with increasingly high workloads. For example, aluminum is a diverse material used in everything from disposable food trays to fitness equipment. It’s in continually high demand, but certain societal trends can make people want it even more.
During the COVID-19 pandemic, many people bought recreational vehicles to travel and stay away from home safely. Aluminum and other metals are key components in RVs.
Some metal fabricators publicly disclose their annual production capabilities. For example, one aluminum company offering billet casting and specialty alloy manufacturing can make more than 200 pounds every year at two of its facilities.
It’s worthwhile for factory leaders to see which factors severely limit production ramp-up efforts. From there, they can further explore how AI and manufacturing automation could reduce those obstacles.
Committing to using AI in manufacturing may encourage metal fabricators to investigate other advanced technologies that could cause a wider future-oriented transformation. For example, 3D printing with metals can create prototypes more quickly or make on-demand products for customers.
Company leaders interested in using AI in manufacturing may even find existing options for combining it with 3D printing. Massachusetts company Markforged has a cloud-based platform for additive manufacturing that uses AI to function. Incorporating machine learning into the product reportedly makes it smarter with every new part produced. The cloud-based model also means that a 3D printer automatically receives software updates.
If metal fabricators are interested in pursuing AI, additive manufacturing or both of those but lack in-house resources, they should think about working with a service provider. Doing that could mean getting access to purpose-built technologies and well-known companies rather than hiring people with the expertise to create the tools from scratch.
Manufacturing automation can also bring significant gains to quality improvement efforts. In one recent example, John Deere partnered with Intel to use AI to spot defective welds on its tractors and other industrial equipment.
Finding defecting welds is a challenging task, especially due to the fast-paced nature of most industrial assembly lines. However, this AI application uses advanced algorithms to detect problematic welds and stop a robotic welder after finding them.
More specifically, a neural network-based inference engine can spot issues in real-time and make the necessary adjustments before continuing. Plus, the computer-vision camera used for this application is just 12-14 inches away from the welded material.
When people want to get quality improvements with AI, choosing metrics to track before moving ahead with any new product is a good idea. Then, it’ll be easier to determine if the expected gains happen to the expected extent.
These potential use cases should help people feel excited about the potential of using AI and other manufacturing automation options to enhance operations. Before finalizing any decisions, the affected parties should remember that the advantages may not be immediately apparent. Still, they typically become obvious if leaders allow enough time to investigate how to best use the technology.
Y Combinator-Backed AI Startup Lofty AI Launches Tokenized, Liquid Marketplace for A.I.-Vetted Real Estate on Algorand
Lofty AI today announces the launch of its liquid real estate marketplace on the Algorand blockchain, including the introduction of a sixth tokenized property listing as it gains momentum in disrupting access to real estate ownership. Lofty AI’s model allows anyone to become a direct owner in real estate and earn rental income for as little as $50 per token and in as few as five minutes, made possible with its blockchain-based solution on Algorand. Unique to Lofty AI, properties listed within the marketplace are vetted by both their local investment team and proprietary artificial intelligence, designed to more accurately evaluate market indicators that drive appreciation, including social media data, retail trends, and more.
Lofty AI’s mission is to bring liquidity and accessibility to the notoriously illiquid real estate market. Algorand’s blockchain technology enables this via minimal transaction fees, advanced smart contracts that allow for the automation of many functions, and industry-leading transaction throughput speed. These factors allow investors to participate with significantly lower minimums and to liquidate their investments at any time of their choosing, thereby reducing risk.
Property tokens on Lofty AI can be purchased via credit card or ACH transfer, and soon, will be eligible for purchase using Algorand-based currencies. Rental income is distributed to owners in USD, with forthcoming options for rental income to be sent directly to a user’s Algorand wallet.
“We believe that real estate investing should not just be reserved for the ultra-wealthy. Our team ran into this issue when investing in real estate personally, so we set out to create a platform that makes it super easy to invest in vetted properties in minutes, for only $50,” said Max Ball, COO of Lofty AI. “Building on top of Algorand was a no-brainer for us––honestly it was probably the easiest decision we’ve had to make so far.”
“We are excited about Lofty AI’s success on Algorand, and congratulate them on today’s sixth tokenized property launch,” said David Markley, Director of Business Solutions at Algorand. “We believe that blockchain technology is the key to democratizing finance, including real estate investment opportunities, and have designed our protocol to facilitate the ease of use, speed, scalability, and true decentralization needed to enable this future.”
The sixth property to be listed on the Lofty AI marketplace goes live today, with 2509 tokens available for investment.
About Lofty AI
Lofty AI lets people invest in tokenized investment properties for only $50. All properties are vetted by their local investment team and proprietary artificial intelligence, and tokens can be sold anytime for no penalties or fees. Lofty AI has raised over $5M in total funding and is backed by leading investors including Y Combinator, Rebel Fund, Jason Calacanis, Hustle Fund, and more.
About Algorand Inc.
Algorand is building the technology to power the Future of Finance (FutureFi), the convergence of traditional and decentralized models into a unified system that is inclusive, frictionless, and secure. Founded by Turing Award-winning cryptographer Silvio Micali, Algorand developed a blockchain infrastructure that offers the interoperability and capacity to handle the volume of transactions needed for defi, financial institutions and governments to smoothly transition into FutureFi. The technology of choice for more than 700 global organizations, Algorand is enabling the simple creation of next generation financial products, protocols and exchange of value.
DeepMotion introduces Markerless Face Tracking
The team at DeepMotion has introduced markerless Face Tracking. The AI-powered motion capture solution is now more complete with the ability to capture full-body motion now with facial expressions – all from a single video.
Adding face capture to Animate 3D gives users more control over expressing their vision by quickly and easily generating 3D face animations in minutes. No special hardware is needed allowing any video captured on any device to be used to generate 3D face animations.
The new face tracking feature is the latest in a continuous and aggressive release schedule that brings new features every few weeks to the service. Some of the other recent features include half-body tracking as well as various foot locking modes that enable users to capture motions like swimming and acrobatics “They’ve been killing it with the updates” says Virtual YouTuber Fruitpex in their recent coverage of top motion capture solutions.
The new face tracking AI captures facial features including blinking, expressive mouth motions, eyebrows and head positions with markerless tracking, no dots necessary. To complement this new feature, they recently launched half-body tracking and tight headshots which further enables tracking of the irises and higher fidelity features of the face. Users can also stick with full-body tracking that will still provide general expressions of character’s faces despite it being further away.
They recommend trying the new face motion capture with their Custom Characters feature to get a sense of what it can look like on a real character. Users can customize their own avatars with the built-in Wolf 3D metaverse avatar creator Ready Player Me, seen recently in other applications like VR Chat. Users can also upload their own unique character directly to the service, however will need to make sure it is set up with the standard ARKit blendshapes for it to work correctly. Alternatively, users can always select the Default character option that will generate the animation on a default character which one can then import into the 3D modeling or animation software of their choice to retarget onto their own character.
All users now have access to this feature, available in the Animation Settings when creating a new animation. The animation download will include the full-body motion data plus facial BlendShape weights based on ARKit Blendshapes. You can check out their FAQ to learn more about how to use Animate 3D Face Tracking for your projects.
Since its inception in 2014, DeepMotion has been on a mission to bring digital characters to life through smarter motion technology. Using physics simulation, computer vision, and machine learning, DeepMotion’s solutions bridge physical and digital motion for virtual characters and machines.
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