Manufacturing Companies Are Slowly Integrating AI

AI is being investigated by many participants in the manufacturing sector, both large and small companies. However, only large companies like Siemens, FANUC, some major robotics companies, and larger automotive and aerospace firms, as well as pharmaceutical companies, can afford to implement AI meaningfully.

AI is still too early in its development cycle to have numerous ready-made applications, making it difficult to implement. AI applications need to be built on a case-by-case basis since there are no off-the-shelf manufacturing applications that use AI natively. As a result, only large companies are currently taking advantage of AI.

Despite this, there is widespread excitement about AI, with many companies starting to use it at the ChatGPT level, such as writing better marketing copy, which is an excellent use case. The challenge, however, is that AI applications are slow to develop because they require a lot of data to be effective. Manufacturing is a great industry for AI as it generates a lot of measurable data and hard facts. But most manufacturing companies are under-digitized, so medium-sized and smaller companies are rapidly trying to digitize their data and create AI-ready repositories. They know they will benefit greatly from AI once they accomplish this, but it is a big and expensive task. Consequently, adoption will be slow, except at the highest levels. – Rhonda Dibachi, CEO at HeyScottie

Hackathons Hit the Wall

2023 and early 2024 saw a raft of internal projects leveraging public AI-as-a-Service vendors for prototyping. However, the gap between prototype and productionisation will lead most of these projects to hit a wall and require working with specialized vendors who can amortize deeper R&D across many customers. – Dev Nag, CEO/Founder at QueryPal

An Influx of Point Solution Companies Implementing AI

The market is still really strong and bullish on GenAI solutions that can create new industries and categories or disrupt existing ones.

Sectors such as medicine, healthcare and financial services are seeing a massive influx of companies creating point solutions that deploy AI in new ways that create value.

For example, tons of companies in the healthcare space are using AI to create new drugs and treatments that would not exist without the massive compute and processing power available to them via the AI boom. – Matt Biringer,CEO at North

Reimaging Computing Experiences and Infrastructure

AI-assisted Linguistic Services in Healthcare

In healthcare, applying AI technology to language and interpreting services has yet to become standard practice when assisting patients with limited English proficiency (LEP) – but that is about to change. It is common knowledge that providers are bound by law to provide linguistics services support to patients in their language of choice. That means that live interpreters typically can be found in hospitals, particularly the emergency room (ER). Beyond the ER, however, most LEP patients are on their own, trying to decipher a hospital menu to order a meal or when simply asking for help. AI can and should be considered to fill patient touchpoint gaps, especially in non-emergent medical situations. Another reason to consider AI application: America is home to 46.2 million immigrants, with over three-quarters holding legal status, marking the highest population in U.S. history as of 2022. Investment in AI-assisted language solutions can help healthcare leaders successfully address three top motives to better serve their non-English-speaking patient population: Cost, efficiency, and quality and engagement. Moreover, AI-assisted enhancements help to level up the quality of the interpreting experience and vastly improve patient compliance and outcomes. – Dipak Patel, CEO at GLOBO Language Solutions

Using AI to Automate Sales

I think one area where we are starting to see the applied use of AI is in the AdTech/MarTech vertical, which is applicable to all businesses and not just travel. Those of us who live and breathe marketing [my entire career has been in technology-based marketing]have now spent the past few years dabbling with generative AI in content creation and workflows. But now, we marketers are looking at how we can automate sales and not just marketing. We have experimented with conversational agents and chat/telephony ourselves, and have seen others experiment here too, with the corporate direction to improve conversion rates and sales success. – John Lyotier, CEO and Founder at TravelAI

Hybrid Switches

As AI continues to advance, hybrid switches that support both PCIe 5.0 and CXL 2.0 will become indispensable in the next generation of AI infrastructure. These hybrid solutions will be the key to overcoming the increasingly complex demands of AI workloads, offering the flexibility to handle both high-speed data transfer and efficient memory sharing. I predict that the adoption of hybrid switches will accelerate, becoming a standard in AI systems, enabling seamless scalability, and future-proofing AI infrastructure across industries. This shift will drive significant innovation, allowing AI applications to reach new heights in performance and efficiency. – Gerry Fan, CEO at XConn Technologies

Enhance Strategic Decision Making With AI Cost Estimation

In the rapidly evolving landscape of IT and digital engineering, we’re seeing a growing demand for cost management technology that allows businesses to streamline projects with AI-driven insights and analysis. The integration of generative AI enables users to leverage sophisticated predictive analytics and machine learning enhancements, so businesses can deliver projects on time, within budget, and with optimal resource utilization. By analyzing extensive historical data, AI models can make highly accurate predictions, learning from past projects to reduce the likelihood of cost overruns.

Its ability to learn and evolve is one of AI’s most compelling features within cost estimation. With each completed project, AI systems refine their algorithms, leading to more accurate estimates in future projects. This continuous improvement is crucial for industries where precision in cost estimation is paramount. Also, AI can continuously update estimates as projects progress and conditions change, such as supply chain disruptions or labor shortages. This approach ensures that estimates remain relevant and accurate throughout the project’s lifespan.

AI has the ability to automate routine and repetitive tasks in cost estimation, which frees up human experts to focus on the more complex and strategic aspects, enhancing overall efficiency. AI also excels in taking into account the unique requirements of each project, including local labor and material costs, to tailor estimates accordingly, ensuring estimates are accurate and relevant to the specifics of each project.

While AI offers a range of advantages in cost estimation, it’s crucial to approach its adoption with a balanced perspective, acknowledging its potential benefits and limitations. Integrating AI in cost estimation is not just about adopting new technology; it’s about enhancing the strategic decision-making process in project management. – Charles Orlando, Chief Marketing Officer at Galorath Incorporated

Federated Learning

Federated Learning is an innovation that is very interesting. Instead of taking all data to one main place for processing, this method lets different devices or servers work together without sharing the raw data directly. It’s a big win for privacy and security, and businesses are loving it.

Federated Learning smartly fixes privacy concerns about data. It allows businesses to use AI capabilities while protecting personal information securely. – Erik Severinghaus, Founder and CEO at Bloomfilter

Edge AI: Smarter Devices Without the Wait

So, you know when you ask your phone how to go somewhere, and it takes a long time to answer? Edge AI is making this better by putting the smart thinking directly on your device. This means quicker replies, less information stored in the cloud, and improved privacy. Shops are using it to guess what you might wish to purchase before you even realize it yourself, making shopping easier and more tailored for each person. It feel like your phone or favorite shop know you more than you know yourself! – Ghazenfer Monsoor, Founder and CEO at Technology Rivers

Organizational Use of AI Forensics and AI Visibility

The technology to secure the generative artificial intelligence (GenAI) organizations are now leveraging has only been around since the first half of 2024. While GenAI’s adoption has become widespread and organizations are seeing its potential for business value, we’re also still learning about the negative impacts of GenAI, how to avoid them along with security risks, and how to ethically harness GenAI’s power.

As we get further into 2024, organizations are going to need to take more proactive approaches to their GenAI applications and strategies to see the full benefits. One example is ensuring both AI forensics and AI visibility capabilities are available across all internal networks. This would look like auditing capabilities of all AI prompts and applications, including traceability, transparency, compliance, and risk management.

Should the worst happen, AI forensics could be a game-changer for organizations by giving them clear visibility into potential risks, tools being used, and who used them, as well as the prompts ingested by the AI models.

Organizations are finding out they cannot manage what they can’t see, making AI forensics and AI visibility a top priority for those looking to ensure even approved GenAI applications don’t pose a potential threat to security posture. –  Arti Raman, CEO and founder at Portal26

Utilizing AI to Improved User Experience

We are now in the phase where the rubber hits the road, lots of customers are realising the promises of AI changing the way they operate was more hype than Truth. The only businesses that have benefited from the huge hype of LLM/Chat GPT etc are the ones that were selling “Shovels in the gold rush” which are Microsoft azure, AWS, Databricks, etc.

But, that said we will see some revolutionary products that are based on improving user experience become even bigger and capturing more market. To take an example perplexity.ai, it is a serious challenger to google. Perplexity with its unique combination of blending search with the power of Large Language models is an awesome win for a new age company battling the behemoths. – Shubh Chatterjee, Founding Scientist at ALgoxlab LLC

 Continued Progress in Quantum AI

I would like to highlight Quantum AI – it’s bound to be a true game-changer in computing. Although still theoretical, combining the principles of quantum mechanics with AI will allow us to process information at speeds and efficiency far beyond traditional computers. This opens the possibility of AI on the proverbial steroids. This is because quantum computers use qubits, which can exist in multiple states at once, such as one and zero, resulting in exponential computing power enabling them to solve complex problems much faster. This capability will enhance AI’s ability to analyze and predict outcomes. It might be the road that takes us to the much-discussed GAI or General Artificial Intelligence – the kind we’ve only seen in sci-fi movies not actual product demos.

The progress of quantum computing has been slow due to the specific conditions required to develop and operate qubits. However, this year has been a banner year for quantum computing, with exciting breakthroughs happening just this summer. Researchers have made significant progress in overcoming a major hurdle: creating stable qubits. One approach utilizes femtosecond lasers for precise manipulation. 

Another breakthrough involves manipulating defects in a silicon crystal lattice, using lasers to create high-quality qubits in silicon by introducing hydrogen atoms into defects. This technique allows for not only creation but also erasure of qubits – key for a more controlled and reliable system.

While it’s still challenging to get qubits to “talk” to each other, for example, these advancements represent significant progress in building a functional quantum computer.

To give you a business case of quantum AI and computing, it could revolutionize pharmaceutical R&D. Traditionally, drug discovery has been painfully slow and expensive. This is because it involves analyzing massive datasets and simulating countless molecular interactions in different scenarios. Quantum AI could accelerate this process severalfold by performing these simulations more efficiently and accurately. Basically, this would allow us to identify promising (and potentially – much more efficient) drug candidates much faster and at a lower cost, which could revolutionize how we develop new medications leading us to genetically personalized medicine etc.- Ilia Badeev, Head of Data Science at Trevolution Group

Data Architectures

In the burgeoning era of data dominance, businesses are keenly pursuing AI integration as a competitive lever, recognizing the necessity of modernizing data architectures to harness the full potential of Generative AI (GenAI) and advanced analytics. This imperative drives a demand for vendors who can deliver foundational technologies—such as robust data management, rapid data transfer, and reliable disaster recovery. As concerns over GenAI misuse persist, the need for secure, recoverable AI data becomes paramount, necessitating advanced data migration technologies and real-time cloud replication to support near-zero recovery time objectives (RTO) and recovery point objectives (RPO). Companies like Microsoft Azure and AWS are pivotal in demystifying AI and crafting tailored AI strategies for businesses, ensuring a seamless blend of AI into their strategic and technological frameworks. Over the next five years, as firms increasingly focus on monetizing AI-driven applications, those vendors that prioritize customer monetization outcomes and can efficiently move, protect, and recover large data sets will likely emerge as leaders. This shift emphasizes not only the technical integration of AI but also strategic alignment with business goals to optimize investment and maximize returns from GenAI initiatives. – Paul Scott-Murphy, Chief Technology Officer at Cirata

Open-source Decentralized AI 

The current trends are 100% around developing an open-source decentralized AI model. The fact that large companies can skew the input models is leading to a full-court press to build out a totally open-source product. Many Depin platforms are a natural fit to deploy this robust decentralized AI model. The future of the people depends on unadulterated input models to ensure rock-solid output models. – Daniel Keller, CEO & Co-founder at InFlux

AI News

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The new resource provides insights on how IT leaders can transform their service management strategies into an enterprise service management (ESM) framework. The firm advises that by integrating advanced technologies like generative AI, IT leaders can ensure their service management practices are not only aligned with organizational objectives but also positioned to drive significant growth and enhance customer experiences in an increasingly competitive market.

“Over the next decade, digital and technology-first services will be how customers and communities primarily engage with your offerings, be that your products, platforms, experiences, support, or any other outcome,” says Carlene McCubbin, associate vice president of research development at Info-Tech Research Group. “Exceptional service then becomes the key differentiator for organizations, prompting technology leaders to shift focus from IT services to enterprise services. In turn, these tech-driven services will bring immense value to customers and your organization.”

Info-Tech’s blueprint emphasizes the transformative impact of exponential technologies on organizations and industries. The firm’s reference to the concept of Exponential IT, which is also a recently revealed research center, is intended to address the ever-increasing demands of today’s organizations. The concept encompasses not just the rapid advances in artificial intelligence and machine learning but also emergent tech trends, digital transformations, and the strategies needed to thrive in an era of exponential technological evolution. With this evolution in mind, Info-Tech details in its latest resource how traditional IT service delivery models may struggle to keep pace with the rapidly evolving needs of organizations and departments as they strive to modernize and remain competitive.

“Delivering exceptional tech-based services requires a complete overhaul, placing the customer at the center of design, orchestration, and delivery,” explains McCubbin. “This transformation demands merging enterprise and business architecture, customer experience, and service planning practices as the foundation for enterprise value.”

Info-Tech recommends organizations shift their focus to an enterprise view of services with customer experience and value at the forefront. This shift can be achieved by setting achievable targets to elevate each service capability and determining actions to reach those targets, including potential use cases for AI. The firm explains that by leading the charge in offering capabilities to the business, IT can evolve from being merely an order-taker or service provider to ultimately co-owning outcomes for the organization.

In Elevate Your Service Capabilities to Drive Enterprise Value, Info-Tech outlines three critical steps IT leaders should follow to transition their services from traditional IT-centric models to enterprise service management practices and drive toward an Exponential IT future:

1. Define a Guiding Star: This step involves IT leaders adopting an Exponential IT mindset, establishing a clear vision and guiding principles, and identifying value and success metrics.
2. Assess Technology and Capability Readiness: IT must identify the current and target state for key ITSM capabilities, assess gaps, and review recommendations to progress each capability through targeted milestones.
3. Build an Effective Roadmap: The final step requires prioritizing initiatives to form a comprehensive roadmap and finalizing it with a robust communication plan.

The comprehensive blueprint provides practical guidance for IT leaders to transform IT service management into a strategic asset. By leveraging exponential technologies and adopting a strategy focused on delivering customer value, the firm explains that IT leaders can ensure their services support organizational growth and drive significant transformation. Embracing this forward-thinking approach will enable organizations to effectively meet future customer expectations, positioning them as pioneers in delivering exceptional digital experiences.

The proactive strategy outlined by Info-Tech in the newly published resource not only enhances service delivery but also fosters long-term innovation and competitiveness in the digital age.

For more information, click HERE.

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At the summit, these leaders came together to engage in critical discourse and gain insights on responsible AI best practices from cross-sector leaders – including representatives from AWS, Verizon, PepsiCo, M&T Bank, Northrop Grumman, and Google DeepMind – and grew their network of responsible AI professionals as they addressed pressing issues surrounding responsible AI governance.

“Internal governance is a key piece in building AI systems that are trustworthy. Once again, our community came together to support soft governance and align on best practices all companies and organizations can and should take to overcome challenges in AI governance,” said EqualAI President and CEO Miriam Vogel. “The critical conversations and alignment on metrics over the course of our summit have taken us a great step forward to achieving safe, responsible, and accessible AI systems that are built for a broader cross section of our population.”

In recent years, many companies have taken the first step of adopting AI principles, but challenges emerge in the next phase of operationalizing those concepts. Often these questions are identical, no matter the size and vertical in which the organization operates. At the summit, business executives – who are developing or using AI for critical functions and are members of EqualAI’s groundbreaking Badge Program, where they are highly trained to implement best practices and allow their brands to be recognized for their responsible and trustworthy practices – spoke amongst key federal policymakers about how to meaningfully lead in this space. This should no longer be an area where companies and organizations are navigating in silos, a key issue that these summits correct.

“I am continuously inspired by the Equal AI Badge Program and its role in shaping ethical AI governance. By collaborating with leading experts, the program continues to advance inclusive and responsible practices and drive substantial impact at PepsiCo and beyond,” said Dr. Athina Kanioura, PepsiCo Chief Strategy and Transformation Officer.

By the concluding remarks of the summit, participants aligned on common practices and challenges around developing, acquiring, and deploying AI systems, specifically, discussing challenges in evaluating models and applications, methods for prioritization as well as metrics and tools for monitoring and evaluation of AI systems. EqualAI plans to release a white paper discussing best practices on these topics and more from the summit in coming months. For more information about the EqualAI summit and ongoing initiatives, please visit https://www.equalai.org/

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Since its introduction in August 2023, Python in Excel has enabled users to perform data manipulation, analysis, and visualization, as well as advanced machine learning and AI tasks, directly within Excel spreadsheets. Until now, the Python code would run on Microsoft Azure’s secure cloud servers. With Anaconda Code, Excel users gain the exclusive ability to run Python code on their local machines without relying on external compute services.

Excel users have long valued security, shareability, and long-term reproducibility in their spreadsheets. Anaconda Code addresses these challenges through its WebAssembly-based technology that enables local, secure Python execution without requiring separate installations or complex environment management. By bridging the gap between traditional spreadsheet use and advanced coding practices, this solution grants users access to a wider Python ecosystem, enhancing data analysis capabilities while maintaining Excel’s core strengths.

“With Anaconda Code, we’re giving users freedom to control the environment,” said Peter Wang, Co-Founder and Chief AI Innovation Officer at Anaconda. “This release marks a significant step forward, enabling Excel users to harness Python’s vast ecosystem while maintaining the speed, reliability and accessibility that businesses and individuals have come to expect from their data tools.”

Expanding Access to Python in Excel 

Users access Anaconda Code via the Anaconda Toolbox in Excel, which democratizes Python use within Microsoft Excel. The Toolbox allows users of all skill levels to generate code and create visualizations efficiently, while simultaneously learning Python. Additionally, the Toolbox facilitates seamless collaboration between Excel users and Python experts through Anaconda.cloud notebooks, enabling efficient data sharing and teamwork.

Excel users with Anaconda Toolbox will now have access to:

• Anaconda Assistant: Utilize AI to analyze tables and suggest data handling methods with history following users across workbooks for consistent code use.
• Code Snippet Management: Write, save, and share Python code snippets directly within Excel, enhancing productivity and collaboration.
• Advanced Visualizations: Create powerful data visualizations using accessible templates and libraries, easily integrated into Excel worksheets.
• Streamlined Data Handling: Use data connectors to access, analyze, and share data in Excel workbooks or Anaconda.cloud notebooks with improved versioning to ensure access to the most current datasets.

Anaconda Code is a direct result of Anaconda’s commitment to its community. Feedback from both Python in Excel users and the broader open-source Python community resulted in a localized Python experience that is widely accessible, easy to use, and secure.

For more information on Anaconda Code and Anaconda Toolbox, visit us HERE. To start using Anaconda Code, download the Anaconda Toolbox in Excel HERE., Microsoft’s add-in marketplace.

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XConn Technologies and MemVerge Demo First Scalable CXL Memory Sharing Solution

The post New Solution to Run Python in Microsoft Excel With Anaconda Code Add-In appeared first on Digital IT News.

CXL ensures memory coherency among CPU memory, CXL-fabric-attached memory pools, and memory on attached devices, enabling resource sharing with unparalleled performance compared to prior interconnect technologies. This breakthrough addresses the challenges faced by AI and HPC, ushering in systems capable of supporting emerging applications in AI and Machine Learning.

The Hybrid Solution for System Design Agility

While the advent of CXL instills hope for AI and HPC developers and users, system designers are still faced with how to meet both current and future interconnect requirements. PCIe (Peripheral component Interconnect Express) remains a widely used interface for connecting hardware components, including GPUs and storage devices. Many traditional applications only need the interconnect capability offered by PCIe. Yet, emergingly, next-generation applications need the higher bandwidth enabled by CXL. Systems designers can be stuck trying to meet demand for the approach with the greatest need.

XConn is meeting this challenge by offering the industry’s first and only hybrid CXL 2.0 and PCIe Gen 5 switch. Combining both interconnect technologies on a single 256-lane SoC, the XConn “Apollo” switch offers the industry’s lowest port-to-port latency and lowest power consumption per port in a single chip – all at a low total cost of ownership.

As a result, system designers only have to design once to achieve versatile expansion, heterogeneous integration for a mix of accelerators and fault tolerance with the redundancy mission critical applications required for true processing availability. Able to serve as a universal interface for CPUs, GPUs, DPUs, FPGAs, and other accelerators, the XConn hybrid switch offers:

• Versatile Expansion: CXL/PCIe switches can be used to connect multiple CXL/PCIe devices mix matched, enabling system designers to create scalable and flexible architectures. This is particularly valuable for HPC clusters and data centers where modular expansion is essential. Even more valuable is the ability to use PCIe and CXL in a single switch where system designers can design hardware once and have the flexibility to support either CXL or PCIe devices by programming the port configuration through software changes.
• Cost Efficiency: As an advanced interconnect solution, the XConn Apollo switch supports both CXL 2.0 and PCIe Gen 5 in a single system-on-chip (SoC). Offering up to 256 lanes, it offers a huge amount of switching capacity that normally requires two or more switches from other vendors – a huge cost savings for system companies on printed circuit board (PCB) area, bunch-of-memory components, and thermal consumption. The hybrid-mode design also offers design savings, where a single hardware design will work with today’s PCIe devices while being future upgradable into a CXL device without re-spinning the PCB design.
• Heterogeneous Integration: Many AI and HPC workloads require a mix of accelerators, such as GPUs, TPUs, and FPGAs. Hybrid PCIe-CXL switches can seamlessly integrate these diverse accelerators taking advantage of the processing performance, scale and lower cost of CXL memory, allowing for heterogeneous computing environments to be optimized for specific tasks.
• Fault Tolerance: The agility of hybrid PCIe-CXL switches can enhance system reliability by providing redundancy and failover capabilities. This is crucial for mission-critical applications where downtime is not an option.

CXL is transforming memory availability for AI and HPC applications. By enhancing memory bandwidth, capacity, and interoperability, CXL promises to unlock the full potential of these memory-intensive workloads. Adoption of this valuable innovation will be accelerated through the delivery of hybrid PCIe-CXL switches which deliver the versatility and efficiency systems designers need to meet diverse use case demands.

To learn more about XConn Technologies hybrid CXL switch technology, visit the website here.

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“We have launched this channel-first strategy at MixMode to meet the rising demand from enterprise organizations whose technologies are failing them in the fight against cyber threats. MixMode’s patented AI generates an evolving alert framework capable of precise real-time detections, uniquely positioning MixMode and its partners to detect and combat the sophisticated cyber attacks of the 21st century including zero-day attacks and ransomware,” said Mark Rotolo, Chief Revenue Officer. “We are excited to empower our partners to deliver more secure solutions leveraging the MixMode Platform.”

The Paradigm program is structured in three tiers for our reseller partner community to reward deeper engagement and investments in MixMode. As well, MixMode offers a managed service partners’ program powered by the platform’s multi-tenant capabilities, which makes our MSPs more efficient from one console and provides a seamless billing solution so they can help customers fight AI-generated and novel attacks with generative AI.

The key benefits of the newly-launched MixMode Paradigm Partner Program Includes:

• Access to the MixMode patented generative AI Platform
• Co-marketing investments for market growth
• Partner enablement to accelerate our joint support in the field with partners
• Access to our newly-launched partner portal with the ability to leverage co-branded vertical technical assets

The MixMode Paradigm Partner Program is designed to open new channels for global customers to harness the breakthrough threat detection and response capabilities MixMode’s patented AI Platform offers for both cloud and on-prem environments. MixMode partners are already seeing service opportunities at around 25% of the total sale, which is driving better margins and overall support for their end-users.

“MixMode gives our clients the ability to detect zero-day attacks and other non-signature-based techniques in the world’s largest datasets before they become a problem,” said Justin Domachowski, CEO and Founder of Defy Security. “This is game-changing protection against increasingly sophisticated adversaries.”

“I’m proud to launch the Paradigm Partner Program and provide our partners with the tools and support they need to take advantage of MixMode’s one-of-a-kind application of generative AI for Cybersecurity threat detection, to benefit and protect our partners’ customers,” said Todd DeBell, VP of Global Channels. “2023 has been a year of investment and growth in MixMode’s channel-first strategy and I am pleased to lead this veteran team of channel professionals. This program enables our partners to quickly leverage the MixMode Platform to deliver advanced threat detection and response at scale to customers across every industry.”

For more information visit the website here.

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Key Features of Scanbase:

Test Result Verification: Using advanced computer vision and AI algorithms, Scanbase can analyze images of Lateral Flow and Colorimetric Tests to ascertain their results, and detect low positives, or faint lines, beyond what the human eye is capable of detecting.

Blood Sample Verification: The company’s groundbreaking technology can validate dried blood spot cards’ adequacy and proper sample collection and drastically reduce errors among sample collection.

What sets us apart?

What sets Scanbase distinctly apart in the market is its plug-and-play medical diagnostic analysis API. This technology offers integration capabilities that companies can leverage within days.

Why Scanbase?

In an era where the demand for at-home medical solutions is surging, Scanbase stands tall with its holistic and easily integrable systems. Its proficiency in Artificial Intelligence (AI), Machine Learning (ML), and vast data handling capabilities guarantee precision and convenience. Besides, Scanbase’s comprehensive solutions range from mobile to web apps, ensuring maximum reach and usability.

Key Benefits:

• Users rejoice with instant at-home results.
• Clients observe an unprecedented enhancement in customer experience.
• Real-time tracking of user results becomes a reality for clients.
• Hospitals and labs receive rapid software upgrades to replace obsolete software

Target Audience & Availability

Scanbase caters predominantly to users of at-home test kits, covering both lateral flow and colorimetric variants, as well as tests that rely on dried blood spot collection. Given its universal applicability, Scanbase proudly announces its global availability, ensuring no boundaries limit its vision of reshaping healthcare. Furthermore, the product provides rapid software upgrade solutions for hospitals and labs aiming to replace their obsolete software.

Scanbase – Verify – Sample Verification

What’s New & Future Endeavours

Recently, we expanded its repertoire and now supports more than 40 at-home lateral flow and colorimetric tests. Additionally, it has incorporated compatibility with all ADx and Whatman style dried blood spot (DBS) cards. Scanbase has also started producing software for hardware analyzers in laboratory and hospital settings.

Words from the CEO

“Our mission is to provide swift and easily accessible healthcare to the global community by building bleeding-edge computer vision and diagnostic software infrastructure,” remarks Jeffrey Lange, CEO of Scanbase.

For more information, including visual aids, testimonials, and detailed insights, please the website here.

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“The unprecedented pace of change and innovations in technologies are transforming the way Healthcare and Life Sciences organizations operate, compete, provide care and bring new treatments to market,” said, Anatoly Postilnik, Managing Director, Clinovera. “Far from being a startup, Clinovera’s experts have decades of domain-specific expertise to help organizations digitally transform to improve the quality of care, speed clinical development, lower costs and ultimately change millions of lives for the better.”

New Clinovera Healthcare and Life Sciences Services:

• Artificial Intelligence and Machine Learning – Clinovera AI/ML experts help organizations analyze, develop and implement rapidly evolving technologies to accelerate the pace of innovation, modernize care infrastructures, and improve population health, patient diagnosis, and drug discovery.
• Real World Evidence (RWE) Research – Clinovera data scientists and software engineers help organizations advance clinical trials and clinical research by building efficient ETL (extract, transfer & load) pipelines to Common Data Models, performing data harmonization, developing high-quality data repositories, and building research applications.
• Data Management, Engineering & Analytics – Clinovera data experts and software engineers help organizations design and build large-scale operational data stores, facilitate data governance, quality management, and generate analytical insights to lower costs and improve patient outcomes.
• Standards-based Health Data Interoperability – Clinovera domain experts analyze, design, develop and implement a highly connected healthcare ecosystem utilizing standard healthcare interoperability protocols and emerging technologies.
• Systems Development and Integration – Clinovera experts and software engineers design, develop and implement clinical decision-support systems, patient and provider portals, and other custom solutions for patients, clinicians, researchers, staff, vendors and others.

Story Highlights:

• The Clinovera brand, to be unveiled at HIMSS23, was created by and is the dedicated Healthcare and Life Sciences division of Cambridge, Mass.-based First Line Software, which has served these industries for more than a decade.
• New industry-specific technology, consulting and advisory services are focused on helping organizations unleash the power of innovative technologies like AI, machine learning, intelligent analytics and others to transform business operations, clinical and research processes and deliver personalized experiences.
• Clinovera’s domain-specific thought leaders, medical informaticians, and other highly skilled technical specialists and developers have decades of experience working with leading Healthcare and Life Sciences organizations.

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This latest financing round was led by Sierra Ventures, with participation from the company’s pre-seed lead investor Origin Ventures and brings its total financing to date to $27 million. Hammoq startup has Automated More than a Half Million Resale Product Listings Across a Broad Network of eCommerce Marketplaces Using Artificial Intelligence and Machine Learning

A glut of online retail returns and a flourishing market for sustainable fashion upcycling and vintage items has created an exploding reCommerce industry. With this financing round, Hammoq will accelerate development of its AI and machine learning platform to automate marketplace listings and open up channels for reseller financing as well as product sourcing.

“The reCommerce market is exploding, with the fashion resale market alone expected to reach $26 billion in 2022,” said Sid Lunawat, CEO and Co-Founder of Hammoq. “There are an abundance of resale marketplaces supporting this economy. Yet, the largest gap is in the labor required to identify product value and push it to the marketplaces. With rising labor costs, the need for automation in the reCommerce industry continues to grow. Using AI and machine learning, Hammoq has put a major dent in this labor gap, empowering its customers to automate the identification and listing process. Our latest funding will accelerate tech development while supporting our sales and marketing strategies to capitalize on this rising opportunity.”

Goodwill of the San Francisco Bay and Hammoq share a deep commitment to sustainability. Hammoq’s AI technology is helping Goodwill to process more material donations, more quickly to help divert more items from landfill. Donated items are given a second life in Goodwill thrift stores.

Another customer of Hammoq is reseller Flip the World. “Before we brought Hammoq on to handle our listings, I would average around 40-50 listings a week. Listing across platforms is time consuming work and after a full day of sourcing, cleaning, prepping and photographing I found that I was falling asleep in bed trying to get my listings up,” said Chris Hatfield, Owner, Flip the World. “Now with Hammoq, we’ve more than doubled our weekly listings and have gone from a 500-item store to over 2,000 in less than two months. It’s been a game changer for our business. Now we can focus on the most important part of reselling, sourcing the items, rather than the listing.”

Hammoq was co-founded by reCommerce industry veterans Sid Lunawat and Ty Blunt, sustainability advocates who believe the 80 billion resalable items dropped in landfills every year have a viable purpose for prolonged use. After successful stints running a reseller business, Lunawat and Blunt understood that identifying and easily listing products for resale was an acute pain point. To solve this challenge, they developed the Hammoq software platform using AI and machine learning to enable listing at scale. Starting with a simple photo, the platform uses product data to automatically enable listings across dozens of resale marketplaces. Hammoq has listed over 600,000 products for sale and ensures 100% compliance with data listing requirements prior to pushing to the marketplaces.

“Hammoq is uniquely serving the exploding reCommerce industry by applying technology to solve sellers’ largest challenge: intelligent, real-time listings,” said Vignesh Ravikumar, Partner, Sierra Ventures. “Their solution and business model are well aligned with our mission to invest early in emerging technology companies that are moving the needle in key market areas. Their SaaS reCommerce solution is delivering precisely what the rapidly transforming market needs as consumers actively seek ways to buy used products that support sustainability and reuse.”

Hammoq is a company that is reimagining reCommerce, digitizing resale goods and automating online listings so customers can list and sell more. Established in 2021 and based in Phoenix, Hammoq has supported the identification and listing automation process for more than 600,000 items. For resellers, liquidators, thrift and retail organizations, Hammoq enables the resale of returns, lost SKUs, overstocks and other pre-loved goods. Built by resellers for resellers Hammoq’s SaaS platform drives customers’ ability to scale reCommerce, while significantly reducing the time and labor needed to get merchandise online.

Sierra Ventures is a Silicon Valley-based early-stage venture firm investing globally with a focus on Core Enterprise and Next-Gen Technologies. With four decades of experience and over $2 billion of assets under management, Sierra has created a vast network of successful entrepreneurs, Global 1000 CXOs, operational executives, and deep domain experts, providing a platform for entrepreneurs around the world.

Learn more about Hammoq at the website here.

Image licensed by unsplash.com

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Retail returns topped $761 billion in 2022 and is projected to reach trillions over the next few years. Due to the complexity of processing these items for resale, merchandise is frequently incinerated, exported, liquidated for pennies on the dollar or dumped into landfills. This is not only unsustainable, it’s costly. As online shopping flourishes and the culture of ‘buy and return’ continues to overwhelm retailers, a resulting used goods marketplace has emerged to a tune of $36 billion in 2022. And that’s just apparel, electronics adds $32.5 billion, used furniture another $12 billion, the list goes on -– placing the resale industry sternly in the trillions.

While there is an abundance of product and a variety of resale marketplaces where these items could be sold, the barrier is in the processing and listing of these products. This has traditionally been a labor-intensive process where products must be manually identified, valued, and characterized before listing for sale. With artificial intelligence (AI) and machine learning, all this is changing.

How AI is Changing the Product Resale Market

Retailers, along with used product resellers, non-profits, thrifters and liquidators, want to capitalize on this thriving reCommerce market to cut the billions of dollars going to waste. AI and machine learning has become the answer. Here are the key ways AI is enabling the reCommerce industry to scale.

1. Attribute Identification. Using AI and machine vision, resellers are able to quickly take what was once a very manual process of identifying and assigning product attributes and automate this identification process. Product characteristics such as color, style and even brand can be quickly assessed, automatically, using simple photographs. This can cut hundreds of hours off product processing when used for large batches of items, such as returns, donations and overstocks.
2. Product Valuation. Returned, donated and used items typically don’t have designated tags or SKUs that can identify the value or assign a price. Using AI technology, identified attributes can be used to scour data sets across resale markets to properly identify current item value and set a market price. This too takes hours off of the traditionally manual product pricing process and is very effective at optimizing resale revenue.
3. Marketplace Listing Automation. Product marketplaces from Poshmark, threadUP and The RealReal to eBay, OfferUp and Facebook Marketplace all require specific adjustments and characterizations to effectively list on their sites. AI technology and automation is being used to produce fully compliant, SEO optimized listings for each marketplace so that products are showcased effectively, no matter where they are being sold.
4. Empowering Listing Volume. Listing products for resale is a numbers game. And with so much product volume to move, retailers specifically need to optimize their listing abilities to recoup lost profits from returned goods. AI empowers scalability and incredible volume, enabling sellers to move from a 5,000-item turn a month to exponentially increase overnight.
5. Lowers Labor Costs. The cost of human labor has been the number one barrier to increasing reCommerce scale. Through AI and machine vision technology, resellers are closing the labor gap and optimizing profitability without the costs, challenge and difficulty of recruiting workers or shipping product offshore to perform processing. This further reduces costs and enhances rapid growth through product processing scale.

There are many uses for AI and machine learning technology, but perhaps none are as market changing as how AI can transform reCommerce. AI bridges the gap between sustainability, labor shortages, and the increasing consumer demand for recycled goods. It will indeed change the game for today’s retailers as they move quickly to compete in today’s competitive retail industry and maximize the losses from high volumes of returns.

For more information please visit the HAMMOQ website.

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