The financing was led by Polaris Partners and managing partner Amy Schulman, who will join the board of directors, with participation from new investors Khosla Ventures, Wittington Ventures and Breakout Ventures. The round was supported by all existing investors DCVC, Zetta Venture Partners, Catalio Capital Management, 11.2 Capital, Epic Ventures, Intermountain Ventures and North South Ventures. The round also included AI funds ApSTAT Technologies, Linearis Labs and Ventures Fund, supported by leading AI expert Yoshua Bengio and metabolomic expert David Wishart, Element AI co-founder Jean-Francois Gagne, and current and former Recursion executives.

Funds from the Series A financing will be used to expand Noetik’s spatial omics-based atlas of human cancer biology (already one of the world’s largest) together with its high throughput in vivo CRISPR Perturb-Map platform. Additionally, the investment will enable the company to scale training of its multi-modal cancer foundation models such as OCTO. The company will leverage these platform capabilities to advance an innovative pipeline of cancer therapeutics candidates to the clinic.

“We are thrilled to have the support of incredible investors who share our vision of combining deep patient data and artificial intelligence to build the future of cancer therapeutics. This significant financing will enable us to accelerate our progress toward turning biological insights into a portfolio of therapeutic candidates” said Ron Alfa, M.D., Ph.D., CEO & Co-Founder, Noetik.

Noetik was founded to solve critically important challenges in bringing effective new therapeutics to patients: improving target discovery and biomarker development to increase the probability of clinical success. To address these, the company has built a discovery and development platform that pairs human multimodal spatial omics data purpose-built for machine learning with a massively multiplexed in vivo CRISPR perturbation platform (Perturb-Map). Together these data are used to train self-supervised foundation models of tissue and tumor biology that power the company’s discovery efforts.

“We are excited to partner with Noetik and support their mission to build a pipeline of potentially transformative cancer programs,” said Amy Schulman, Managing Partner, Polaris Partners. “We have been investing in the most innovative life science technologies for decades and have been excited about the potential of AI. Noetik impressed us both with the sophistication of their platform and the team’s dedication to make an impact for patients.”

The company aims to establish strategic partnerships and collaborations with leading academic institutions, health care providers, and pharmaceutical companies. The company recently appointed Shafique Virani, M.D. as the company’s Chief Business Officer to spearhead these partnering efforts.

“We are thrilled to continue backing Noetik. The team’s speed of execution in building one of the most sophisticated AI-enabled oncology discovery engines in less than two years is unprecedented, and their deep experience and demonstrable progress have only strengthened our conviction,” said James Hardiman, General Partner, DCVC.

Noetik is committed to advancing the field of precision oncology and improving outcomes for cancer patients worldwide. This Series A funding marks a significant milestone in the company’s journey and reinforces its position as a leader in the development of AI-driven cancer therapies.

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Introduction 

Large Language Models (LLMs) are considered to be an AI revolution, altering how users interact with technology and the world around us. Especially with deep learning algorithms in the picture data, professionals can now train huge datasets that will be able to recognize, summarize, translate, predict, and generate text and other types of content.

As LLMs become an increasingly important part of our digital lives, advancements in natural language processing (NLP) applications such as translation, chatbots, and AI assistants are revolutionizing the healthcare, software development, and financial industries.

Therefore, to get a closer view of the challenges, we will discuss the four limitations of LLMs devise a decision to eliminate those limitations, and focus on the benefits of LLMs. 

1. Limitations of LLMs in the Digital World

We know that LLMs are impressive technology, but they are not without flaws. Users often face issues such as contextual understanding, generating misinformation, ethical concerns, and bias. These limitations not only challenge the fundamentals of natural language processing and machine learning but also recall the broader concerns in the field of AI. Therefore, addressing these constraints is critical for the secure and efficient use of LLMs. 

Let’s look at some of the limitations:

1.1. Contextual Understanding

LLMs are conditioned on vast amounts of data and can generate human-like text, but they sometimes struggle to understand the context. While humans can link with previous sentences or read between the lines, these models battle to differentiate between any two similar word meanings to truly understand a context like that. For instance, the word “bark” has two different meanings; one “bark” refers to the sound a dog makes, whereas the other “bark” refers to the outer covering of a tree. If the model isn’t trained properly, it will provide incorrect or absurd responses, creating misinformation.

1.2. Misinformation 

Even though LLM’s primary objective is to create phrases that feel genuine to humans; however, at times these phrases are not necessarily to be truthful. LLMs generate responses based on their training data, which can sometimes create incorrect or misleading information. It was discovered that LLMs such as ChatGPT or Gemini often “hallucinate” and provide convincing text that contains false information, and the problematic part is that these models point their responses with full confidence, making it hard for users to distinguish between fact and fiction.

1.3. Ethical Considerations 

There are also ethical concerns related to the use of LLMs. These models often generate intricate information, but the source of the information remains unknown, hence questioning its transparency in its decision-making processes. To add to it, there is less clarity on the source of these datasets when trained, leading to creating deep fake content or generating misleading news.

1.4. Potential Bias

As LLMs are conditioned to use large volumes of texts from diverse sources, they also carry certain geographical and societal biases within their models. While data professionals have been rigorously working to keep the systems diplomatic, however, it has been observed that LLM-driven chatbots tend to be biased toward specific ethnicities, genders, and beliefs.

2. Addressing the Constraints of LLMs

Now that we have comprehended the limitations that LLMs bring along, let us peek at particular ways that we can manage them:

2.1. Carefully Evaluate  

As LLMs can generate harmful content, it is best to rigorously and carefully evaluate each dataset. We believe human review could be one of the safest options when it comes to evaluation, as it is judged based on a high level of knowledge, experience, and justification. However, data professionals can also opt for automated metrics that can be used to assess the performance of LLM models. Further, these models can also be put through negative testing methods, which break down the model by experimenting with misleading inputs; this method helps to pinpoint the model’s weaknesses.

2.2. Formulating Effective Prompts 

The way users phrase the prompts, the LLMs provide results, but with the help of a well-designed prompt, they can make huge differences and provide accuracy and usefulness while searching for answers. Data professionals can opt for techniques such as prompt engineering, prompt-based learning, and prompt-based fine-tuning to interact with these models.

2.3. Improving Transparency and Removing Bias

It might be a difficult task for data professionals to understand why LLMs make specific predictions, which leads to bias and fake information. However, there are tools and techniques available to enhance the transparency of these models, making their decisions more interpretable and responsible. Looking at the current scenario, IT researchers are also exploring new strategies for differential privacy and fairness-aware machine learning to address the problem of bias.

Final Thoughts

LLMs have been transforming the landscape of NLP by offering exceptional capabilities in interpreting and generating human-like text. Yet, there are a few hurdles, such as model bias, lack of transparency, and difficulty in understanding the output, that need to be addressed immediately. Fortunately, with the help of a few strategies and techniques, such as using adversarial text prompts or implementing Explainable AI, data professionals can overcome these limitations. 

To sum up, LLMs might come with a few limitations but have a promising future. In due course of time, we can expect these models to be more reliable, transparent, and useful, further opening new doors to explore this technological marvel.

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Today, MLCommons® announced new results for its industry-standard MLPerf® Inference v4.1 benchmark suite, which delivers machine learning (ML) system performance benchmarking in an architecture-neutral, representative, and reproducible manner. This release includes first-time results for a new benchmark based on a mixture of experts (MoE) model architecture. It also presents new findings on power consumption related to inference execution.

MLPerf Inference v4.1

The MLPerf Inference benchmark suite, which encompasses both data center and edge systems, is designed to measure how quickly hardware systems can run AI and ML models across a variety of deployment scenarios. The open-source and peer-reviewed benchmark suite creates a level playing field for competition that drives innovation, performance, and energy efficiency for the entire industry. It also provides critical technical information for customers who are procuring and tuning AI systems.

The benchmark results for this round demonstrate broad industry participation, and includes the debut of six newly available or soon-to-be-shipped processors:

• AMD MI300x accelerator (available)
• AMD EPYC “Turin” CPU (preview)
• Google “Trillium” TPUv6e accelerator (preview)
• Intel “Granite Rapids” Xeon CPUs (preview)
• NVIDIA “Blackwell” B200 accelerator (preview)
• UntetherAI SpeedAI 240 Slim (available) and SpeedAI 240 (preview) accelerators

MLPerf Inference v4.1 includes 964 performance results from 22 submitting organizations: AMD, ASUSTek, Cisco Systems, Connect Tech Inc, CTuning Foundation, Dell Technologies, Fujitsu, Giga Computing, Google Cloud, Hewlett Packard Enterprise, Intel, Juniper Networks, KRAI, Lenovo, Neutral Magic, NVIDIA, Oracle, Quanta Cloud Technology, Red Hat, Supermicro, Sustainable Metal Cloud, and Untether AI.

“There is now more choice than ever in AI system technologies, and it’s heartening to see providers embracing the need for open, transparent performance benchmarks to help stakeholders evaluate their technologies,” said Mitchelle Rasquinha, MLCommons Inference working group co-chair.

New mixture of experts benchmark

Keeping pace with today’s ever-changing AI landscape, MLPerf Inference v4.1 introduces a new benchmark to the suite: mixture of experts. MoE is an architectural design for AI models that departs from the traditional approach of employing a single, massive model; it instead uses a collection of smaller “expert” models. Inference queries are directed to a subset of the expert models to generate results. Research and industry leaders have found that this approach can yield equivalent accuracy to a single monolithic model but often at a significant performance advantage because only a fraction of the parameters are invoked with each query.

The MoE benchmark is unique and one of the most complex implemented by MLCommons to date. It uses the open-source Mixtral 8x7B model as a reference implementation and performs inferences using datasets covering three independent tasks: general Q&A, solving math problems, and code generation.

“When determining to add a new benchmark, the MLPerf Inference working group observed that many key players in the AI ecosystem are strongly embracing MoE as part of their strategy,” said Miro Hodak, MLCommons Inference working group co-chair. “Building an industry-standard benchmark for measuring system performance on MoE models is essential to address this trend in AI adoption. We’re proud to be the first AI benchmark suite to include MoE tests to fill this critical information gap.”

Benchmarking Power Consumption

The MLPerf Inference v4.1 benchmark includes 31 power consumption test results across three submitted systems covering both datacenter and edge scenarios. These results demonstrate the continued importance of understanding the power requirements for AI systems running inference tasks, as power costs are a substantial portion of the overall expense of operating AI systems.

The Increasing Pace of AI Innovation

Today, we are witnessing an incredible groundswell of technological advances across the AI ecosystem, driven by a wide range of providers including AI pioneers; large, well-established technology companies; and small startups.

MLCommons would especially like to welcome first-time MLPerf Inference submitters AMD and Sustainable Metal Cloud, as well as Untether AI, which delivered both performance and power efficiency results.

“It’s encouraging to see the breadth of technical diversity in the systems submitted to the MLPerf Inference benchmark as vendors adopt new techniques for optimizing system performance such as vLLM and sparsity-aware inference,” said David Kanter, Head of MLPerf at MLCommons. “Farther down the technology stack, we were struck by the substantial increase in unique accelerator technologies submitted to the benchmark this time. We are excited to see that systems are now evolving at a much faster pace – at every layer – to meet the needs of AI. We are delighted to be a trusted provider of open, fair, and transparent benchmarks that help stakeholders get the data they need to make sense of the fast pace of AI innovation and drive the industry forward.”

View the Results

To view the results for MLPerf Inference v4.1, please visit the Datacenter and Edge benchmark results pages.

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nOps, the leading AWS cost optimization platform, today announced the closing of a $30 million Series A funding round led by Headlight Partners. nOps is empowering organizations across the globe to solve one of the largest IT challenges of the last decade – better understanding, controlling and reducing cloud spend. With the industry’s most comprehensive suite of visibility and automation tools for optimizing AWS cloud costs, the nOps platform is the only end-to-end solution that provides a holistic look into all factors of cloud optimization.

According to Gartner, worldwide end-user spending on public cloud services is forecast to grow by 20.4% in 2024, surpassing $675 billion. However, 30% of that spending is wasted on underutilized cloud resources, and 20% goes toward costly On-Demand pricing. This means organizations are leaving billions of dollars on the table. In fact, as many as 80% of companies report that they consistently go over budget on their cloud spend. nOps enables organizations to optimize AWS cloud costs to better align with strategic computing needs.

A FinOps Foundation member, nOps’ end-to-end platform, unlike point solutions, gives FinOps, DevOps, Engineering, and Finance teams complete visibility into their AWS costs. The platform uses artificial intelligence (AI) and machine learning (ML) to analyze compute needs and automatically optimize it for efficiency, reliability and cost. With awareness of all your AWS commitments and the AWS Spot market, nOps automatically fulfills your commitments and provisions additional compute to Spot. Further, with the rise in AI and generative AI specifically, cloud usage and costs are increasing. The nOps platform makes it easy to track and allocate AI workloads. nOps helps its clients manage more than $1.5 billion of AWS cloud spend, and has grown its customer base by 450% over the past 18 months.

“Cloud usage, particularly with the emergence of compute-heavy AI workloads, has reached a tipping point. While various point solutions address specific cloud optimization needs, engineering teams do not have the time to manually manage and optimize the ever-growing complexity of cloud resources. Instead, they need one solution that provides complete visibility into cloud spend, automatic optimization and single-click cloud waste clean up so they can focus on innovation to drive company growth. This is why we founded nOps and why we have been so successful,” said JT Giri, CEO and founder of nOps. “With the support from Headlight Partners and our other investors, this funding will help us meet the growing demand for our FinOps platform. By empowering our customers to reliably optimize their AWS cloud usage and costs, while increasing productivity for developers and engineers, nOps is turning IT back into an innovation driver – not a cost center.”

By automatically optimizing an organization’s compute resources and spending, the nOps platform is different from other cloud and spend management offerings. The platform features three distinct solutions that deliver a more comprehensive approach to controlling AWS cloud spending, including:

• Business Contexts provides visibility into all AWS spending, from the largest resources to container costs – it automates and simplifies AWS cost allocation and reporting.
• Compute Copilot intelligently manages and optimizes autoscaling technologies to ensure the greatest efficiency and stability at the lowest costs.
• Cloud Optimization Essentials automates time-consuming cloud cost optimization tasks, including resource scheduling and rightsizing, stopping idle instances, and optimizing Amazon Elastic Block Storage (EBS) volumes.

“nOps has built a proven platform that its customers love and we are thrilled to partner with the company on its next phase of growth,” said Jack Zollicoffer, Co-Founder at Headlight Partners. “We see organizations struggle to rein in AWS cloud spending. nOps brings a unique, more holistic approach that marries optimizing cloud cost while ensuring reliable availability of compute services. This provides its nOps customers with the confidence that they’ll never pay more than necessary for the cloud services required to run their business.”

The new capital will be used to accelerate the development of nOps’ industry-leading FinOps platform, further expand integrations with AWS products and open-source technologies like Karpenter, and improve the customer experience.

nOps seamlessly integrates and automatically optimizes Amazon Elastic Kubernetes Service (EKS), Amazon EC2 Auto Scaling Groups (ASG), Amazon Elastic Container Service (ECS), and Karpenter – setting it apart in the market.

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AI-powered e-commerce platforms scale SMB operations by providing sophisticated pricing analysis and inventory management. Encryption and blockchain applications significantly mitigate concerns about data security and privacy by enhancing data protection and ensuring the integrity and confidentiality of information.

A 2024 survey of 530 small and medium-sized businesses (SMBs) reveals that AI adoption remains modest, with only 39% leveraging this technology. Content creation seems to be the main use case, with 58% of these businesses leveraging AI to support content marketing and 49% to write social media prompts.

Despite reported satisfaction with AI’s time and cost-saving benefits, the predominant use of ChatGPT or Google Gemini mentioned in the survey suggests that these SMBs have been barely scratching the surface of AI’s full potential. Indeed, AI offers far more advanced capabilities, namely pricing analysis and inventory management. Businesses willing to embrace these tools stand to gain an immense first-mover advantage.

However, privacy and security concerns raised by many SMBs regarding deeper AI integration merit attention. The counterargument suggests that the e-commerce platforms offering smart pricing and inventory management solutions would also provide encryption and blockchain applications to mitigate risks. 

Regressions and trees: AI under the hood

Every SMB knows that setting optimal product or service prices and effectively managing inventory are crucial for growth. Price too low to beat competitors, and profits suffer. Over-order raw materials, and capital gets tied up unnecessarily. But what some businesses fail to realize is that AI-powered e-commerce platforms can perform all these tasks in real time without the risks associated with human error.

At the center is machine learning, which iteratively refines algorithms and statistical models based on input data to determine optimal prices and forecast inventory demand. The types of machine learning models employed vary across industries, but two stand out in the context of pricing and inventory management.

Regression analysis has been the gold standard in determining prices. This method involves predicting the relationship between the combined effects of multiple explanatory variables and an outcome within a multidimensional space. It achieves this by plotting a “best-fit” hyperplane through the data points in a way that minimizes the differences between the actual and predicted values. In the context of pricing, the model may consider how factors like region, market conditions, seasonality, and demand collectively impact the historical sales data of a given product or service. The resulting best-fit hyperplane would denote the most precise price point for every single permutation or change in the predictors (which could number in the millions).

What machine learning contributes to this traditional tried-and-true econometric technique is scope and velocity. Whereas human analysts would manually deploy this tool within Excel, using relatively simple data sets from prior years, machine learning conducts regression analysis on significantly more comprehensive data sets. Moreover, it can continuously adapt its analysis in real-time by feeding it the latest data. This eliminates the need for a human to spend countless hours every quarter redoing the work.

In summary, machine-learning regression ensures that price points are constantly being updated in real time with a level of precision that far surpasses human capability.

As for inventory management, an effective methodology within machine learning’s arsenal would be decision trees.

Decision trees resolve inventory challenges using a flowchart-like logic. The analysis begins by asking a core question, such as whether there is a need to order more products to prevent understocking. Next, a myriad of factors that are suspected to have an effect on this decision are fed to the model, such as current stock, recent sales, seasonal trends, economic influences, storage space, etc. Each of these factors become a branch in the decision tree. As the tree branches out, it evaluates the significance of each factor in predicting the need for product orders against historical data. For example, if data indicates that low stock levels during certain seasons consistently lead to stockouts, the model may prioritize the “current stock” branch and recommend ordering more products when stock levels are low during those seasons.

Ultimately, the tree reaches a final decision node where it determines whether to order more products. This conclusion is based on the cumulative analysis of all factors and their historical impact in similar situations.

The beauty of decision trees is that they provide businesses an objective decision-making framework that systematically and simultaneously weigh a large number of variables — a task that humans would struggle to replicate given the large volumes of data that must be processed.

The machine learning techniques discussed earlier are just examples for illustration purposes; real-world applications are considerably more advanced. The key takeaway is that e-commerce platforms offering AI-powered insights can scale any SMB— regardless of its needs.

Balancing AI with data security

With great power comes great responsibility, as the saying goes. An e-commerce platform harnesses the wondrous capabilities of AI must also guarantee the protection of its users and customers’ data. This is especially relevant given that AI routinely accesses large amounts of data, increasing the risk of data breaches. Without proper security measures, sensitive information can be exposed through cyber-attacks.

When customers are browsing an online marketplace, data privacy and security are top of mind. According to a PwC survey, 71% of consumers will not purchase from a business they do not trust. Along the same lines, 81% would cease doing business with an online company following a data breach, and 97% have expressed concern that businesses might misuse their data.

Fortunately, e-commerce platforms provide various cybersecurity measures, addressing security compromises and reassuring both customers and the SMBs that host their products on these platforms.

Encryption is a highly effective method for securing data transmission and storage. By transforming plaintext data into scrambled ciphertext, the process renders the data indecipherable to anyone without the corresponding decryption key. Therefore, even if hackers somehow manage to intercept data exchanges or gain access to databases, they will be unable to make sense of the data. Sensitive information such as names, birthdays, phone numbers, and credit card information will appear as meaningless jumble. Research from Ponemon Institute shows that encryption technologies can save businesses an average of $1.4 million per cyber-attack.

Block chain technology contributes an extra level of security to e-commerce platforms. Transaction data is organized into blocks, which are in turn linked together in a chain. Once a block joins the chain, it becomes difficult to tamper with the data within. Furthermore, copies of this “blockchain” are distributed across multiple systems worldwide so that the latter can detect any attempts to illegitimately access the data. An IDC survey suggests that American bankers are the biggest users of block chain, further underscoring confidence in this technology.

The argument here is that SMBs can enjoy the benefits of AI while maintaining data privacy and security. The right e-commerce platforms offer tried-and-true measures to safeguard data and prevent breaches.

Having your cake and eating it too

The potential of AI in SMBs remains largely untapped. As such, those daring enough to exploit machine learning to empower their business logics may reap a significant dividend over competitors who insist on doing things the old-fashioned way. By automating essential functions like pricing analysis and inventory management, businesses can achieve unprecedented levels of efficiency and accuracy. The e-commerce platforms providing these services are equipped with robust cybersecurity features, providing valuable peace of mind for SMBs.

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Codenotary, leaders in software supply chain protection, today announced that the company has closed the first half of 2024 with record sales and strong expansion into the financial services and government segments, as well as into the defense vertical with best-in-class supply chain security platform, Trustcenter.

“The first half of 2024 saw Codenotary close several multi-year agreements with world-class customers in financial and government sectors. We grew equally in the U.S. and European markets. Overall, we are very pleased with the number of new customers and the 140% sales growth in the U.S. and Europe. After a profitable 2023, we achieved further margin expansion in the first half of 2024. Some of our customers now secure billions of artifacts in their DevOps environments,” said Moshe Bar, CEO of Codenotary.

Codenotary secured so far $25 million in financing, and added customers that include some of the largest banks in the U.S. and Europe, along with government, pharmaceutical, industrial, and defense clients. Helping the company grow in every region, Codenotary has a network of 80 resellers in the U.S. and Europe.

“Large-scale organizations have realized the need to secure their software supply chain given the pervasive attacks of the last few years targeting software components. The biggest threat to the application security of organizations today comes from within. It’s too easy to import malicious code through a quick library import in a project. It takes mere seconds to infect the DevOps environment,” said Dennis Zimmer, co-founder and CTO of Codenotary.

“The British Army has extremely high standards for excellence and security in our computing environment. Codenotary has been responsive to our needs, and their product fits our stringent requirements,” said Captain D. Preuss, British Army.

In terms of product development, the company made three releases of its flagship product, Trustcenter, including version 4.8 which became the first such platform to add machine learning to automate the process of recognizing security issues which are exploitable in the customer’s environment. Codenotary’s Trustcenter is typically used as part of an organization’s compliance, auditing, and forensics activity to maintain a secure software supply chain. Significantly, it increases overall application security by enforcing that only trusted and approved artifacts are built into apps.

Codenotary is also the primary maintainer of immudb, the first and only open-source enterprise-class immutable database with data permanence at scale for demanding applications — up to billions of transactions per day. There have been more than 40 million downloads of immudb to date, which serves as the foundation for Codenotary’s supply chain security products. Thousands of organizations worldwide use immudb to secure their data from tampering rather than cumbersome and complex blockchain technologies.

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“Mike’s breadth of experience across the B2B enterprise software lifecycle will be critical as HiddenLayer executes on its mission to protect the machine learning models behind today’s most important products,” said Chris Sestito, CEO and Co-founder of HiddenLayer. “His expertise will play a key role in accelerating our product roadmap and enhancing our ability to defend enterprises’ AI models against various threats.”

Bruchanski joins HiddenLayer from Elementary, where he was Vice President of Product, driving the advancement of the company’s offerings and market growth. Previously, he held similar roles at Blue Lava, Inc., where he shaped the product vision and strategy, and at Cylance, where he managed the company’s portfolio of OEM products and partners.

With a strong foundation in engineering, holding degrees from Villanova University and Embry-Riddle Aeronautical University, Mike combines a technical background with experience in scaling organizations’ product strategies. His leadership will be invaluable as HiddenLayer continues to innovate and protect AI-driven systems.

“The acceleration of AI has introduced new vulnerabilities and risks in cybersecurity. I’m excited to join the talented team at HiddenLayer to develop solutions that meet the complex challenges facing enterprise customers today,” said Bruchanski.

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Greetings Robert, Could you please share with us your professional journey and how you came to your current role as Chief Innovator of Monjur?

Thank you for having me. My professional journey has been a combination of law and technology. I started my career as an intellectual property attorney, primarily dealing with software licensing and IT transactions and disputes.  During this time, I noticed inefficiencies in the way we managed legal processes, particularly in customer contracting solutions. This sparked my interest in legal tech. I pursued further studies in AI and machine learning, and eventually transitioned into roles that allowed me to blend my legal expertise with technological innovation. We founded Monjur to redefine legal services.  I am responsible for overseeing our innovation strategy, and today, as Chief Innovator, I work on developing and implementing cutting-edge AI solutions that enhance our legal services.

How has Monjur adopted AI for streamlined case research and analysis, and what impact has it had on your operations?

Monjur has implemented AI in various facets of our legal operations. For case research and analysis, we’ve integrated natural language processing (NLP) models that rapidly sift through vast legal databases to identify relevant case law, statutes, and legal precedents. This has significantly reduced the time our legal professionals spend on research while ensuring that they receive comprehensive and accurate information. The impact has been tremendous, allowing us to provide quicker and more informed legal opinions to our clients. Moreover, AI has improved the accuracy of our legal analyses by flagging critical nuances and trends that might otherwise be overlooked.

Integrating technology for secure document management and transactions is crucial in today’s digital landscape. Can you elaborate on Monjur’s approach to this and any challenges you’ve encountered?

At Monjur, we prioritize secure document management and transactions by leveraging encrypted cloud platforms. Our document management system utilizes multi-factor authentication and end-to-end encryption to protect client data. However, implementing these technologies hasn’t been without challenges. Ensuring compliance with varying data privacy regulations across jurisdictions required us to customize our systems extensively. Additionally, onboarding clients to these new systems involved change management and extensive training to address their concerns regarding security and usability.

Leveraging cloud platforms for remote collaboration and accessibility is increasingly common. How has Monjur implemented these platforms, and what benefits have you observed in terms of team collaboration and accessibility to documents and resources?

Monjur has adopted a multi-cloud approach to ensure seamless remote collaboration and accessibility. We’ve integrated platforms like Microsoft, GuideCX and Filevine to provide our teams with secure access to documents, resources, and collaboration tools from anywhere in the world. These platforms facilitate real-time document sharing, and project management, significantly improving team collaboration. We’ve also implemented granular access controls to ensure data security while maintaining accessibility. The benefits include improved productivity, as our teams can now collaborate efficiently across time zones and locations, and a reduced need for physical office space, resulting in cost savings.

In what ways is Monjur preparing for the future and further technological advancements? Can you share any upcoming projects or initiatives in this regard?

At Monjur, we’re constantly exploring emerging technologies to stay ahead. We continue to training our Lawbie document analyzer and are moving toward our goal of being able to provide real-time updates to our clients legal documents.  

As the Chief Innovator, what personal strategies do you employ to stay abreast of the latest technological trends and advancements in your field?

To stay current, I dedicate time each week to reading industry reports, academic papers, and blogs focused on AI, machine learning, and legal tech. I also attend webinars, conferences, and roundtable discussions with fellow innovators and ch leaders. Being part of several professional networks, provides me with valuable insights into emerging trends. Additionally, I engage in continuous learning through online courses and certifications in emerging technologies. Lastly, I maintain an open dialogue with our  team and regularly brainstorm with them to uncover new ideas and innovations.

What advice would you give to our readers who are looking to integrate similar technological solutions into their organizations?

My advice would be to start by identifying your organization’s pain points and evaluating how technology can address them. Engage your teams early in the process to ensure their buy-in and gather their insights. When selecting technology solutions, prioritize scalability and interoperability to future-proof your investments. Start small with pilot projects, measure their impact, and scale up based on results. It’s also crucial to foster a culture of continuous learning and innovation within your organization. Finally, don’t overlook the importance of data security and compliance, and ensure that your solutions align with industry standards and regulations.

With your experience in innovation and technology, what are some key factors organizations should consider when embarking on digital transformation journeys?

Embarking on a digital transformation journey requires a clear strategy and strong leadership. Here are some key factors to consider:

1. Vision and Objectives: Clearly define your vision and set measurable objectives that align with your overall business goals.
2. Change Management: Prepare for organizational change by fostering a culture that embraces innovation and training teams to adapt to new technologies.
3. Stakeholder Engagement: Involve all stakeholders, including clients, to ensure their needs and concerns are addressed.
4. Technology Selection: Choose technologies that offer scalability, interoperability, and align with your specific business requirements.
5. Security and Compliance: Implement robust security measures and ensure compliance with relevant data protection laws.
6. Continuous Improvement: Treat digital transformation as an ongoing process rather than a one-time project. Regularly assess the impact of implemented solutions and refine your strategy accordingly.

By considering these factors, organizations can navigate the complexities of digital transformation more effectively and reap the full benefits of their technological investments.

Robert Scott

Chief Innovator at Monjur

Robert Scott is Chief Innovator at Monjur.  He provides a cloud-enabled, AI-powered legal services platform allowing law firms to offer long-term recurring revenue services and unlock the potential of their legal templates and other firm IP. redefines legal services in managed services and cloud law. Recognized as Technology Lawyer of the Year, he has led strategic IT matters for major corporations,  in cloud transactions, data privacy, and cybersecurity. He has an AV Rating from Martindale Hubbell, is licensed in Texas, and actively contributes through the MSP Zone podcast and industry conferences. The Monjur platform was recently voted Best New Solution by ChannelPro SMB Forum. As a trusted advisor, Robert navigates the evolving technology law landscape, delivering insights and expertise.

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The growing importance of agility and operational efficiency has helped introduce serverless solutions as a revolutionary concept in today’s data processing field. This is not just a revolution, but an evolution that is changing the whole face of infrastructure development and its scale and cost factors on an organizational level. Overall, For companies that are trying to deal with the issues of big data, the serverless model represents an enhanced approach in terms of the modern requirements to the speed, flexibility, and leveraging of the latest trends.

1. Understanding Serverless Architecture

Working with serverless architecture, we can state that servers are not completely excluded in this case; instead, they are managed outside the developers’ and users’ scope. This architecture enables developers to be detached from the infrastructure requirements in order to write code. Cloud suppliers such as AWS, Azure, and Google Cloud perform the server allocation, sizing, and management.

The serverless model utilizes an operational model where the resources are paid for on consumption, thereby making it efficient in terms of resource usage where resources are dynamically provisioned and dynamically de-provisioned depending on the usage at any given time to ensure that the company pays only what they have consumed. This on-demand nature is particularly useful for data processing tasks, which may have highly varying resource demands.

2. Why serverless for data processing?

2.1 Cost Efficiency Through On-Demand Resources 

Old school data processing systems commonly involve the provision of systems and networks before the processing occurs, thus creating a tendency to be underutilized and being resource intensive. Meanwhile, server-less compute architectures provision resources in response to demand, while IaaS can lock the organization in terms of the cost of idle resources. This flexibility is especially useful for organizations that have prevaricating data processing requirements.

In serverless environments, cost is proportional to use; this means that the costs will be less since one will only be charged for what they use, and this will benefit organizations that require a lot of resources some times and very few at other times or newly start-ups. This is a more pleasant concept than servers that are always on, with costs even when there is no processing that has to be done.

2.2 Scalability Without Boundaries

 Extreme success in autoscaling is one of the greatest strengths of serverless architectures. When the data processing tasks in question may have unpredictable bursts of data – for example, when you have to process a great number of records at once, or run periodic batch commands, having the serverless architecture like AWS Lambda or Azure Functions will automatically scale to meet the demand that has to be processed. However, even in large fields, this scalability shall not only be characterized by the ability to manage the huge amounts of data generated but also the extent to which this will be possible with very little delay and at the highest possible level of efficiency.

Since the massive compilation can be processed in parallel, they get around the limitations of traditional architectural structures and deliver insights much earlier. This is important, especially for firms that depend on input processing for real-time data processing for decision purposes, such as firms dealing in finance, e-commerce, and IoT.

2.3 Simplified Operations and Maintenance

Outsourcing server management makes it easier for teams to focus on the creation of central functions for the application rather than being overwhelmed with technological issues. As for deployment, updates, and monitoring, serverless architectural approaches provide inherent tools that make these operations easy.

Including a scaling of application, self-healing properties, and runtime environments imply that operational management is kept to a minimum. In data processing, it means more efficacious and predictable utilization as the infrastructure learns in an instant about the requirements of the application.

2.4 Innovation Through Agility 

Serverless architectures enable the use of possible multi-tenant services because they introduce possible custom deployment for new compute-oriented data processing workloads. There are no expensive configurations, no infrastructure to purchase, which must also be repaid in the long run, and no time-consuming installation.

Serverless functions are easily designed to work independently with a very loose coupling; thus, it follows the microservices model, whereby the various components of a system, in this case a data pipeline, can be developed and deployed independently. This kind of agility is important specifically for organizations that, in one way or another, have to quickly respond to market shifts or have to incorporate new technologies into their processes.

2.5 Security and Compliance 

Security and compliance are not a factor of discussion when it comes to data processing and management. Serverless architectures have microservices and management instruments that include auto-update, patch, and encrypt, as well as the privilege controls. The underlying infrastructure of a multi-tenant cloud is secured by the cloud providers so that organizations can focus on data and application logic.

Moreover, commonly used serverless solutions are compliance-certified solutions, so businesses do not have to search for compliance themselves. This is especially valid when it comes to fields such as finance, medicine, or government, which require high levels of compliance when it comes to data processing.

3. Advanced Use Cases of Serverless Data Processing

3.1 Real-Time Analytics 

Integration of real-time analytics requires that the data is analyzed as soon as it is received, making serverless architecture suitable because of its scalability for throughput and low latency. Some of the use cases that could be well served by this kind of application are fraud detection, stock trading algorithms, and real-time recommendation engines.

3.2 ETL Pipelines 

Data acquisition, preparation, and loading procedures are collectively referred to as Extract, Transform, Load (ETL) workflows. Serverless architectures enable As such, there is an opportunity to process large data volumes in parallel with ETL jobs to become faster and cheaper. The fact of scaling and resource management, which is provided by serverless platforms, allows achieving accumulation of ETL processes and their working without interruptions and slowdowns with regard to the size of the load.

3.3 Machine Learning Inference 

Deploying a model for inference on a serverless platform is much cheaper and quicker than deploying them on a conventional platform. In serverless architectures, resources are also self-sufficient when it comes to the computing needs of complex models, thus enabling easy deployment of machine learning solutions at scale.

4. Overcoming Challenges in Serverless Data Processing

Despite the numerous benefits provided by serverless architectures, there are some issues that need to be discussed. There could be increased cold start latency because when the function is invoked for the first time, it takes more time to bring up resources due to this latency, which can be an issue in latency-sensitive systems. On top, due to the stateless nature of serverless functions, stateful operations can be challenging and hence may have to be handled outside the functions by using resources such as databases.

Nonetheless, these concerns could be addressed through architectural guidelines, for instance, applying warm-up techniques for lessening the cold start time or employing managed stateful services that can easily connect to serverless functions.

5. Looking Ahead: The Future of Serverless Data Processing

In the era where more and more large and small organizations turn to serverless solutions, the approaches to data processing will inevitably change as well. When serverless computing is married with other technologies such as edge computing, artificial intelligence, and blockchain, then it opens up new prospects for data processing.

The change to serverless is not just technical anymore, but rather a significant change in organizational adoption of both platforms and applications. Those pursuing the art of decision-making based on big data will also need to adopt serverless architectures to survive in the long run.

6. Strategic Leverage for Competitive Advantage

Serverless architectures provide the organizations an edge to survive in the ever-increasing digital economy environment. Since serverless models are more cost-effective, easily scalable, and operate in a highly efficient manner, companies need to unlock the ability to process data in near real-time and progress the innovation curve even further. As it stands today, data has become a new form of oil that cannot be converted into the physical world, but rather, a form of oil that, in the modern world, cannot be processed and analyzed without the need for a specialized set of tools. Subsequently, as the world continues to advance digitally, serverless architectures will not bypass the chance to better the existing peculiarities of data processing.

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Piers, congratulations on your appointment as the new CEO of The ai Corporation. Can you share your vision for leading the organization into the fuel and mobility payments sector?

Our vision at The ai Corporation (ai) is to revolutionise the retail fuel and mobility sector with secure, efficient, and seamless payment solutions while leading the charge against transaction fraud. ai delivers unparalleled payment convenience and security to fuel retailers and mobility service providers, enhancing the customer journey and safeguarding financial transactions. 

We believe in fuelling progress by simplifying transactions and powering every journey with trust and efficiency. In an era where mobility is a fundamental aspect of life, we strive to safeguard each transaction against fraud, giving our customers the freedom to move forward confidently. We achieve that by blending innovative technology and strategic partnerships and relentlessly focusing on customer experience: 

Seamless Integration: We’ve developed an advanced payment system tailored for the fuel and mobility sector. By embracing technologies like EMV and RFID, we ensure contactless, swift, and smooth transactions that meet our customers’ needs. Our systems are designed to be intuitive, providing easy adoption and enhancing the customer journey at every touchpoint.

Unmatched Security: Our robust fraud detection framework is powered by cutting-edge AI, meticulously analysing transaction patterns to identify and combat fraud pre-emptively. We’re committed to providing retailers with the knowledge and tools to protect themselves and their customers, fostering an environment where security and vigilance are paramount.

With the increasing demand for sustainable fuels and EV charging, how do you plan to address potential fraud and fraudulent data collection methods in unmanned EV charging stations?

The emergence of new and the continued growth of existing sustainable fuels means our experts are constantly identifying potential risks and methods of exploitation proactively. The increase in unmanned sites is particularly challenging as we observe a steady rise in fraudulent activity that is not identifiable within payment data, such as false QR code fraud. In these circumstances, our close relationships with our fuel retail customers enable us to utilise additional data to identify at-risk areas and potential points of compromise to assist in the early mitigation of fraudulent activity.

Mobile wallets are on the rise in fleet management. How do you navigate the balance between convenience for users and the potential risks of fraud and exploitation associated with these payment methods?

When introducing any new payment instruments, it is critical to balance the convenience of the new service with the potential risk it presents. As with all fraud prevention strategies, a close relationship with our customers is vital in underpinning a robust fraud strategy that mitigates exposures, while retaining the benefits and convenience mobile wallets offer. Understanding the key advantages a fleet management application brings to the end user is vital for understanding potential exposure and subsequent exploitation. That information enables us to utilise one or multiple fraud detection methods at our disposal to mitigate potentially fraudulent activity whilst balancing convenience and flexibility.

The trend of Abuse of Genuine fraud is noticeable despite advancements in mobile wallet payments. How do your AI-driven scoring systems combat this complex fraud type in the industry?

Our teams identify Abuse of Genuine fraud by using enhanced behavioural profiling across extended periods and utilising sector-specific data in full to enable us to create a detailed and accurate profile for both payment instruments and vehicles. Industry-specific data, for example, from fleet odometers, is exceptionally valuable when you are developing a behavioural profile for a specific vehicle. Combined with other methods, this enables us to quickly identify areas of increased spending or a change of spending profile. That insight is vital when identifying Abuse of genuine fraud, as this type of fraud is often perpetrated for long periods of time and in very high volumes.

Opportunistic fraud and overclaiming by legitimate customers can inflate fraudulent values. How can businesses enhance confidence in point-of-compromise identification and distinguish genuine customer behavior from fraudulent activity?

The short answer is that businesses need to ensure that they are working with experts who understand fraud and understand the impact that false positives can have on a fraud strategy. Incorrectly identified fraudulent transactions affect bottom-line losses and can severely harm a business fraud strategy and AI Scoring Systems. 

As a result, we firmly hold that visualising precise trend profiles and pinpointing potential compromise points are as critical as receiving the initial fraud alert. By combining industry-specific data with payment and transaction information, we can often clearly identify deviations from legitimate activities through proper visualisation. This forensic approach enhances our ability to understand and act on fraudulent behaviour effectively.

With the move to open-loop payment capabilities, what measures need to be taken to address the increased fraud and security risks associated with this wider acceptance payment instrument?

Robust security measures are crucial as open-loop payments gain traction in the fleet and mobility sectors:

• Multi-factor authentication, including biometrics, verifies user identity. 
• Machine learning analyzes transactions for suspicious patterns. 
• Encryption and tokenization protect sensitive data. 
• Fraud management systems monitor transactions and notify users of suspicious activity. 
• User and employee education on fraud tactics strengthen security. 
• Collaboration between payment providers allows for sharing best practices and adhering to industry regulations like PCI DSS, creating a secure payment environment. 

These efforts balance security with convenience to ensure safe user experiences.

Innovation is key in the fuel and mobility sectors. How does your technology contribute to fraud prevention while engaging directly with end-users, encouraging community growth, and promoting interaction with brands?

ai’s advanced technology has been developed to shield the fuel and mobility sectors from fraud. Our machine learning detects suspicious transactions, fake accounts, and identity theft in real-time, protecting businesses and helping them stay ahead of evolving fraudster tactics.

In addition to providing our users with a comprehensive rules management platform, our sophisticated fraud management solutions deploy machine learning to optimise rules in production, recommend new rules, and identify underperforming ones to remove. 

We model data in real time to enable probabilistic scoring or transactions to assess the likelihood they are fraudulent, allowing authorisation decisions to be taken in real-time to prevent fraud. By leveraging advanced algorithms and machine learning, our clients can stay ahead of fraudsters.

Our technology also ensures data quality by distinguishing deliberate fraud from genuine mistakes. This empowers businesses to make accurate fraud decisions. Additionally, our collaboration across industries strengthens the fight against fraud through shared solutions and regulations.

Beyond security, our technology fosters positive brand-consumer relationships – enabling our users to provide personalized experiences, loyalty programs, and feedback mechanisms to build a strong community with their customers.

Technology protects against fraud, ensures data reliability, and facilitates meaningful interactions between brands and their communities. By embracing innovation, businesses can safeguard operations while promoting growth and trust.

As vehicles become payment mechanisms, what security considerations and fraud prevention strategies should businesses adopt, especially in the context of innovations like integrating payment choices into vehicles?

As vehicles evolve into payment mechanisms, retailers need to put in place robust security measures and fraud prevention strategies to ensure the safety of financial transactions. Some payment security measures to consider include:

• Encryption – Employ robust encryption protocols to protect sensitive data during transmission and prevent unauthorized access.
• Tokenisation – replacing actual payment card details with tokens – unique identifiers that are useless to fraudsters even if intercepted.
• Secure communication channels – ensuring secure communication between vehicles and payment gateways to prevent/deter unauthorised use.
• Authentication – implementing multi-factor authentication to verify users’ identities will prevent the unauthorized use of payment instruments.
• Secure Hardware – consider using tamper-resistant hardware for payment processing within vehicles.

In terms of fraud prevention strategies, key considerations should include:

• Fraud detection systems – leveraging advanced machine learning algorithms to identify suspicious patterns and activities.
• Know Your Customer (KYC) – Deploy rigorous KYC practices to help verify user identities and prevent fraudulent transactions and account abuse.
• Regulatory compliance – adhering to industry standards and regulations to maintain a secure payment environment is a must, including PCI DSS compliance.
• Customer education – education of end users around safe payment practices and potential risks is the front line for fraud prevention.
• Behavioural analysis – monitoring user behaviour to detect anomalies – which can be enhanced and automated by using machine learning detection models.
• Real-time alerts – setting up real-time alerts to end users for unusual transactions or activities.
• Geolocation verification – validating the location of the transaction against the vehicle’s actual position.
• Device fingerprinting – creating unique fingerprints for each device to detect suspicious behaviour.

Businesses must adopt a holistic, layered approach that combines robust security practices, fraud prevention strategies, and regulatory compliance adherence to safeguard financial transactions while integrating payment choices into vehicles.

Tokenization is being considered to fight fraud. How do you approach this technology, considering potential regulatory requirements, and what implications do you foresee for PSD3?

Tokenization combats payment fraud by replacing sensitive data with meaningless tokens during transactions. This protects actual card details and can also be applied to other sensitive data.

New European regulations (PSD3) emphasize security and user privacy, aligning well with tokenization’s benefits. PSD3 is expected to tighten security measures further and encourage anti-fraud technologies.

While tokenization enhances security, regulations like PSD3 may not definitively address liability for fraudulent token transactions. As tokenization becomes more widespread, clear guidelines for such cases will be essential.

There is no doubt that tokenization is a powerful tool against fraud, but balancing security, innovation, and user rights will be essential for any robust payment ecosystem to comply with PSD3.

How do you foresee intelligent fuel management and predictive vehicle maintenance playing a role in fraud prevention and operational efficiency within the fuel and mobility sectors?

Intelligent fuel management and vehicle maintenance powered by AI are revolutionizing transportation. Businesses can optimise fuel usage, predict maintenance needs, and prevent fraud by analysing vast amounts of data, ultimately that translates to reduced costs, improved efficiency, and a more sustainable future.

Here’s how:

• AI optimises routes: Real-time traffic data helps choose the most efficient paths, saving fuel and time.
• Predicting demand patterns: Businesses can anticipate needs and strategise fuel management across different transportation modes, streamlining inventory control.
• Enhanced supply chain resilience: AI forecasts disruptions, identifies inefficiencies, and tracks inventory for better preparedness.
• Proactive vehicle maintenance: Sensor data helps detect potential problems before they become major breakdowns, reducing downtime and repair costs.
• Preventing fuel theft: In-vehicle sensors monitor fuel levels and detect unauthorised access, ensuring fuel security.

Intelligent fuel management and predictive maintenance create a win-win situation for businesses and the environment.

Piers Horak

Chief Executive Officer at The ai Corporation 

Piers Horak is Chief Executive Officer of The ai Corporation (ai). Horak brings over 15 years of extensive expertise in enterprise retail payments, banking, and fraud prevention. Horak is responsible for building on ai’s track record of developing innovative technology that allows its clients and their customers to take control and grow profitably by managing omnichannel payments and stopping fraud.

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