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.

Explore AITechPark for top AI, IoT, Cybersecurity advancements, And amplify your reach through guest posts and link collaboration.

The post Overcoming the Limitations of Large Language Models first appeared on AI-Tech Park.

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.

The post AITech Interview with Robert Scott, Chief Innovator at Monjur first appeared on AI-Tech Park.

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.

Explore AITechPark for top AI, IoT, Cybersecurity advancements, And amplify your reach through guest posts and link collaboration.

The post The Rise of Serverless Architectures for Cost-Effective and Scalable Data Processing first appeared on AI-Tech Park.

Introduction

Data lineage tools are sophisticated software designed for complete data management within the organizational context. These tools’ primary role is to systematically record and illustrate the course of data elements from their source through various stages of processing and modification, ultimately reaching the pinnacle in their consumption or storage. They can help your organization to understand and manage data. However, currently, you will find a lot of data lineage tool alternatives out there, but no worries, as AITech Park has narrowed down the best option for your company that will help you this year.

1. Collibra

Collibra is a complete data governance platform that incorporates data lineage tracking, data cataloging, and other features to assist organizations in managing and using their data assets more effectively. The platform features a user-friendly interface that can be easily integrated into other data tools, aiding data professionals to describe the structure of data from various sources and formats. Collibra provides companies with a free trial, but the pricing depends on the needs of your company.

2. Gudu SQLFlow

Gudu SQLFlow is one of the best data lineage analysis tools. It interprets SQL script files, obtains data lineage, conducts visual display, and permits users to provide data lineage in CSV format and conduct visual display. SQLFlow delivers a visual representation of the overall flow of data across databases, ETL, business intelligence, cloud, and Hadoop environments by parsing SQL scripts and stored procedures. Gudu SQLFlow offers a few pricing options for data lineage visualization, including a basic account, a premium account ($49 per month), and an on-premise version ($500 per month).

3. Alation

The third one on our list is Alation, which is a data catalog that helps data professionals find, understand, and govern all enterprise data in a single. The tool uses ML to index and make new data sources such as relational databases, cloud data lakes, and file systems. With Alation, data can easily be democratized, which gives quick access alongside metadata to guide compliant, intelligent data usage with vital context. However, the plan and pricing are not revealed by Alation, as it depends on the needs of your company.

4. Atlan

Atlan ranks fourth in our list of the best data lineage tools as it delivers outstanding capabilities in four key areas. These include data cataloging and finding, data quality and research, data lineage and governance, and data exploration and integration. Apart from these, Atlan enables users to handle data usage and adoption across the ecosystem with granular governance and access controls, no matter where the data flows.

5. Dremio

Lastly, we have Dremio, which is a data lake engine that delivers fast query speeds and a self-service semantic layer that works directly on data lake storage. The tools are connected with S3, ADLS, and Hadoop, making it a complete package. With collaboration with Apache Arrow, data reflection, and other Dremio technologies work wonders and further speed up queries, and the semantic layer allows IT to apply security and business implications.

Conclusion

Choosing the correct data lineage tool requires assessing all factors that are well aligned with your company’s data management objectives. Therefore, before opting for any tool from the above list, consider taking data from diverse sources, formats, and complexity and creating a data governance framework, policies, and roles that eventually help in making informed decisions.

Explore AITechPark for top AI, IoT, Cybersecurity advancements, And amplify your reach through guest posts and link collaboration.

The post The Five Best Data Lineage Tools in 2024 first appeared on AI-Tech Park.

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.

The post AITech Interview with Piers Horak, Chief Executive Officer at The ai Corporation first appeared on AI-Tech Park.

As organizations continue doubling their reliance on data, the question of having credible data becomes more and more important. However, with the increase in volume and variety of the data, high quality and keeping track of where the data is coming from and how it is being transformed become essential for building credibility with the data. This blog is about data quality and data lineage and how both concepts contribute to the creation of a rock-solid foundation of trust and reliability in any organization.

1. The Importance of Data Quality

Assurance of data quality is the foundation of any data-oriented approach. Advanced information’reflects realities of the environment accurately, comprehensively, and without contradiction and delays.’ It makes it possible for decisions that are made on the basis of this data to be accurate and reliable. However, the use of inaccurate data leads to mistakes, unwise decisions to be made, and also demoralization of stakeholders.

1.1 Accuracy: 

Accuracy, as pertains to data definition, means the extent to which the data measured is actually representative of the entities that it describes or the conditions it quantifies. Accuracy in numbers reduces the margin of error in the results of analysis and conclusions made.

1.2 Completeness: 

Accurate data provides all important information requisite in order to arrive at the right decisions. Missing information can leave one uninformed, thus leading to the wrong conclusions.

1.3 Consistency: 

It makes data consistent within the different systems and databases within an organization. Conflicting information is always confusing and may not allow an accurate assessment of a given situation to be made.

1.4 Timeliness: 

Data is real-time; hence, decisions made reflect on the current position of the firm and the changes that are occurring within it.

2. The Role of Data Lineage in Trust and Reliability

Although data quality is a significant aspect, data provenance, data lineage, and data destination are equally significant factors. This is where data lineage comes into play. Data lineage, therefore, ensures that one knows the lineage of the data, the point of origination, how it evolved, and the pathways it has been through. Data lineage gives a distinct chain of how a piece of data comes through an organization right through to its utilization.

2.1 Traceability: 

Data lineage gives organizations the ability to trace data to its original source. Such traceability is crucial for verifying the correctness as well as accuracy of the data collected.

2.2 Transparency: 

As a result, one of the most important advantages of using data lineage is better transparency within the company. The company ensures that the stakeholders have an insight into how the data has been analyzed and transformed, which is important in building confidence in the data.

2.3 Compliance: 

Most industries are under the pressure of strict data regulations. Data lineage makes compliance easy for an organization in that there is accountability for data movement and changes, especially when an audit is being conducted.

2.4 Risk Management: 

Data lineage also means beneficial for defining the risks for the data processing pipeline. It is only by becoming familiar with the data’s flow that an organization can easily identify any issues, such as errors or inconsistencies, before arriving at the wrong conclusion based on the wrong data.

3. Integrating Data Quality and Data Lineage for Enhanced Trust

Data quality and data lineage are related and have to be addressed together as part of a complete data management framework. Here’s how organizations can achieve this:

3.1 Implement Data Quality Controls: 

Set up certain policies in the process of data management at each phase of the process. Conduct daily, weekly, monthly, and as needed check-ups and data clean-ups to check if the data is of the needed quality.

3.2 Leverage Data Lineage Tools: 

Ensure that software selection for data lineage gives a graphical representation of the flow of data. These tools are quite useful for monitoring data quality problems and the potential effects of such changes on the data.

3.3 Foster a Data-Driven Culture: 

Promote use of data within the organization, which would ensure that high importance is placed on the quality and origin of such data. Also, explain to the employees the relevance of these ideas and the part they play in the success of any business.

3.4 Continuous Improvement: 

Data quality and lineage are not just activities that are done once but are rather cyclical. Ensure that the quality of data management is consistent with an ongoing process of active monitoring of new developments in the business environment and new trends and possibilities offered by technology.

4. Parting Words

When data is being treated as an important company asset, it becomes crucial to maintain the quality of the data and to know its origin in order to build its credibility. Companies that follow data quality and lineage will be in a better position to take the right decisions, follow the rules and regulations set for them, and be in a better position compared to their competitors. If adopted in their data management process, these practices can help organizations realize the full value of their data, encompassing certainty and dependability central to organizational success.

Explore AITechPark for top AI, IoT, Cybersecurity advancements, And amplify your reach through guest posts and link collaboration.

The post Focus on Data Quality and Data Lineage for improved trust and reliability first appeared on AI-Tech Park.

Table of Contents
Introduction
1. Ruby on Jets
2. AWS Amplify
3. Architect
4. Pulumi
5. Zappa
Conclusion

Introduction

In the digital world, the serverless framework is one of the most innovative technologies that allows software developers (SDEs) to build and deploy applications without the requirement to address the underlying server infrastructure.

Numerous organizations are gradually switching to serverless computing frameworks as they help them achieve faster, simpler software development and eliminate traditional monolithic software models. However, implementing serverless computing SDEs requires frameworks that will help them to focus solely on writing code to implement their application’s logic.

In this article, we’ll explore the top five serverless frameworks that SDEs can use to deploy code faster and scale seamlessly.

1. Ruby on Jets

Software developers who have expertise in the Ruby language and wish to develop applications in this language can opt for Ruby on Jets. Jets further have unique functionalities that can be used to assemble diverse AWS resources. This tool aids in the creation and deployment tasks of programs employing SQS, DynamoDB, AWS Lambda, SNS, and many more. 

2. AWS Amplify

With the AWS Amplify framework, SDEs can rapidly create robust serverless applications for web apps and enjoy unlimited versatility. With a few taps, you can supervise and launch single-page web applications, static websites, server-side-produced applications, and status web pages. Using this application’s intelligent processes, you can easily set up your serverless backends with information, storage, and authorization. 

3. Architect

Architect is a comprehensive framework that uses AWS, node.js, NPM, and other languages to create applications. It is an open-source serverless platform with more than 30 collaborators on GitHub, keeping it safe and reliable to use. It is also quite user-friendly for novice developers, aiding them to operate faster and adapt to changes easily. This framework has the potential to build, operate, and manage serverless applications and further simplifies the configuration and provisioning.

4. Pulumi

The Pulumi framework is an open-source tool to create, deploy, and manage cloud-based software. The software uses existing computer languages, native toolkits, and frameworks for YAML, and a few domain-specific languages such as TypeScript, JavaScript, Python, Go, and .NET for coding. Pulumi can ease AWS, Azure functions, GCP, and Kubernetes platform management duties as it simplifies the installation and maintenance of Lambda features.

5. Zappa

Zappa is one of the prominent serverless frameworks, as it aims to be quite prevalent for web-based applications as well as possibly even. It offers a perfect interface for re-platforming systems that rely on things such as Flask apps. For instance, if you are operating on a Flask app, try to involve Zappa; it allows SDEs to leverage AWS Lambda and API gateways without having to modify a significant amount of coding. Zappa offers improved security since it permits the identity and access management (IAM) security technique by the standard.

Conclusion

As modern technologies grow rapidly, it can be challenging for developers to maintain pace with them. Therefore, the above five serverless frameworks aim to enable faster and more seamless serverless deployment. However, these applications might differ in terms of technicalities and use cases; therefore, software developers must consider factors such as supported programming languages, community, pricing model, execution time, and control to select the right serverless frameworks.

Explore AITechPark for top AI, IoT, Cybersecurity advancements, And amplify your reach through guest posts and link collaboration.

The post The Top Five Serverless Frameworks to Look for in 2024 first appeared on AI-Tech Park.

Greetings George, Could you please share with us your professional journey and how you came to your current role as Chief Technology Officer at Upwave?

My professional journey has been a bit unconventional. I studied philosophy at Yale, not expecting to go into tech. I ended up working as an investment analyst, doing a lot of data analysis and model building on topics like how government stimulus impacts the economy.

Realizing finance wasn’t my passion, I started a music startup that ultimately didn’t pan out. Nearly 8 years ago, I joined Upwave as a software engineer focused on data. Over time, I became manager of our data team, director of engineering, VP of engineering, and now CTO overseeing all product and technology.

Given the urgency of embracing AI-driven strategies, how has Upwave integrated artificial intelligence into its operations to drive innovation and maintain a competitive edge?

AI is deeply integrated into everything we do at Upwave. We’ve long used what I call “predictive AI” – machine learning techniques that have existed for decades. From the beginning, we built ML and statistical analysis algorithms to optimize ad campaigns.

In the last year and a half, we’ve also embraced the newer “generative AI” exemplified by tools like ChatGPT. Over the past 9 months, we’ve leveraged generative AI to help customers get more value from the brand measurement we provide. Just yesterday, we launched the open beta of our AI Campaign Insights Reports, which use generative AI to synthesize and summarize campaign results into easy-to-understand language, charts, and actionable recommendations. We’re very excited about it.

Revolutionizing brand ROI through marketing measurement is a critical aspect of modern business. Could you provide insights into Upwave’s approach to this and the impact AI-driven marketing analytics have had on enhancing brand performance?

Modern brand campaigns are extremely complex, with vast amounts of money spent across dozens or even hundreds of channels. It’s simply too much for humans to track and optimize manually. That’s where AI shines – at digesting huge datasets to find mathematical optimizations and make concrete recommendations to improve cost-effectiveness and ROI.

Upwave takes a two-pronged AI approach: First, using predictive AI for high-quality measurement and clear analysis of ROI opportunities. Second, leveraging generative AI to communicate those opportunities to customers as clearly and actionably as possible.

We’ve found that customers who lean into this data-driven, AI-powered approach are seeing dramatic performance improvements and ROI increases. The combination of unique data, insightful analysis, and powerful communication enabled by AI is a game-changer.

As the CTO at Upwave, what are your insights into the future of brand analytics, and how do you foresee AI shaping the landscape in the coming years?

I’m a huge believer in the potential of generative AI. It’s easy to assume today’s capabilities are all these systems will ever have, but having worked in AI for over 15 years, I’ve seen firsthand how these tools continuously improve in compounding, accelerating ways. I’m very bullish.

Even if AI never advances further, it will still become widely deployed across brand advertising – analytics, media planning, creative, and so on. But these tools will only get more powerful over time, perhaps dramatically so.

I anticipate AI will substantially augment more and more day-to-day activities in advertising. Organizations that embrace AI, understand how to harness it, and adapt their workflows will become far more productive, efficient, and effective. Those resistant to change will be left behind.

The transformative potential of GenAI is fascinating. How do you anticipate AI advancements surpassing human capabilities and reshaping marketing content creation strategies at Upwave?

As I said, I expect these AI tools to improve dramatically over time, taking on more functions humans currently handle. But I see this more as AI augmenting rather than replacing humans.

Consider how a bulldozer is far more efficient than a human at clearing a construction site, yet you still have a human operating the bulldozer. One human with a bulldozer can now do the work of dozens or hundreds in far less time.

I expect a similar dynamic with AI – it will replace certain aspects of jobs humans do, but humans will still orchestrate the AI to achieve their goals, just far more efficiently. People will be able to produce more and better advertising, then use tools like Upwave to measure performance and continuously optimize in a virtuous cycle.

So while advertising will become much more automated, there will still be an important role for humans in guiding the process.

In your role as Chief Technology Officer, what personal strategies do you employ to stay informed about the latest advancements in AI and technology, ensuring Upwave remains at the forefront of innovation?

I’m rather obsessive about keeping up with AI given how transformative I believe it will be for both tech and advertising.

For me, this means firsthand experience – regularly using tools like ChatGPT, Claude, Anthropic, paying for premium versions, participating in hackathons to build hands-on AI projects. I even recently won the TED AI hackathon with a team.

Secondly, I stay closely involved with Upwave’s AI product development, frequently discussing capabilities and limitations with our engineers and PMs.

To track the cutting edge, for all its issues, I find Twitter an excellent source of direct info from top researchers. I also subscribe to several great newsletters that summarize key AI news.

It takes a multi-pronged approach as the AI field is moving extremely rapidly, but falling behind would be a huge risk.

What advice would you offer to our audience, particularly businesses seeking to integrate AI-driven strategies into their marketing and brand analytics efforts?

Take AI very seriously. It’s going to significantly impact nearly every business. Yes, there are dodgy “AI” products and snake oil salesmen out there, but that doesn’t negate the genuine value being created by real AI capabilities. And these tools are only going to get better over time.

Even if AI can’t perfectly solve your needs today, that may change by next month. Effectively applying AI still takes skill, and initial attempts may not pan out, but that doesn’t mean AI can’t provide huge value with the right approach.

I strongly recommend building real AI expertise, either in yourself or your organization. Understand both the potential of today’s tools and how that will evolve going forward. Your competitors certainly are. Allowing them to gain an AI advantage now risks them leaving you in the dust as that edge compounds.

So be discerning, but don’t dismiss AI. The risk of ignoring it is too great.

With your extensive experience in technology and marketing, what key considerations should companies keep in mind when implementing AI solutions for marketing purposes?

First, be thoughtful about applying AI to problems. Naively throwing AI at an issue without carefully considering the problem space and the model’s constraints can lead to reputational or even legal issues, as these systems can be erratic.

However, you also can’t overcorrect and entirely avoid AI just because it involves some risk. Throughout history, many valuable technologies have been dangerous when misused but extremely beneficial when wielded properly.

Within 5 years, I believe it will be nearly impossible to remain competitive in marketing without heavy AI usage. Building those capabilities now will be key to keeping pace.

Even with today’s AI tools, if applied effectively, there is substantial potential to unlock. Tools like Upwave can help you increase your ROI by 2-3 times, for instance. So there’s already a lot of value to capture from existing tools, and that will only grow.

Can you share a success story or milestone where Upwave has effectively utilized AI-driven strategies to enhance brand ROI or marketing performance?

Absolutely. While I can’t name names, Upwave offers Persuadability Scores which directly measure brand advertising’s impact, similar to tracking clicks or conversions for direct response.

We worked with a major financial services advertiser and DSP to feed in these AI-generated metrics, allowing the DSP to steer ads towards the highest-impact, best-ROI opportunities. The result was material performance improvements for campaigns using these Persuadability Scores. That’s a great example of predictive AI boosting marketing effectiveness.

On the generative AI side, our new AI Campaign Insights Reports provide easily digestible summaries and recommendations that enable customers to align internal stakeholders and optimize in-flight campaigns.

The substantial leverage of generative AI makes it far more time and cost efficient to perform the necessary analysis and communication to understand campaign performance and disseminate those insights to key decision-makers. We’re seeing strong customer uptake and satisfaction so far.

Finally, considering your expertise, what are your reflections on the future of AI in marketing, and any additional insights you’d like to share with our audience?

As I’ve touched on, AI capabilities are going to advance substantially, likely in ways that eventually unsettle people as AIs become able to handle much of the work humans currently do. In certain domains, AIs will simply outperform even the most skilled humans.

This means very significant changes are coming to marketing and advertising, whether we want them or not. These are global technological forces beyond any individual company’s control.

In this sense, an AI tsunami is approaching. We can either learn to surf that wave or get crushed by it, but we can’t stop it.

So it’s crucial for businesses of all kinds to very seriously consider how they’ll navigate the AI-transformed future and hopefully leverage AI as a competitive advantage. Because organizations that fail to appreciate the gravity of this shift are in for an extremely challenging decade ahead.

George London

Chief Technology Officer at Upwave

George is a seasoned technology leader who has spent his whole career helping companies use data to make better decisions. George started his career doing macroeconomic modeling and investment research at Bridgewater Associates (the world’s largest hedge fund), and then founded a startup that used data to help consumers explore and discover music.

As one of Upwave’s first engineering hires, George originally joined Upwave with the mission of building Upwave’s statistical capabilities from scratch. Since then he’s grown with the company to become Head of Data, then Vice President of Engineering, and now CTO. In his years at Upwave, George has both contributed to nearly every aspect of Upwave’s systems and product and has also hired, managed, and coached Upwave’s entire technical team.

George holds a BA in Philosophy from Yale University and lives in Oakland with his wife and labradoodle.

The post AITech Interview with George London, Chief Technology Officer at Upwave first appeared on AI-Tech Park.

Kiranbir, we’re delighted to have you at AITech Park, could you please share your professional journey with us, highlighting key milestones that led you to your current role as a Senior Staff Engineering Manager at Google?

I started as a software engineer at Garmin then Apple. As I grew my career at Apple, I wanted to help and lead my peers the way my mentors helped me. I also had an arrogant epiphany about how much more I could get done if I had a team of people just like me. That led to my first management role at Microsoft.

Initially, I found it challenging to balance my desire to have my team work my way with prioritizing their career growth. Eventually, I was responsible for a program where I had to design, develop, and ship an accessory for the Hololens in only six months. I was forced to delegate and let go of specific aspects and realized I was getting in the way of progress. 

My team was delivering amazing solutions I never would have thought of. I realized I didn’t need to build a team in my image. I had hired a talented team with unique skills. My job now was to empower them and get out of their way. This realization was eye-opening and humbled me.

I also realized the skills I used for engineering weren’t the same skills I needed to be an effective leader. So I started focusing on being a good manager. I learned from even more mistakes over the years and ultimately established three core values for every team I lead:

1. Trust your team and peers, and give them autonomy.
2. Provide equity in opportunity. Everyone deserves a chance to learn and grow.
3. Be humble.

Following my growth as a manager, Microsoft presented me with several challenges and opportunities to help struggling teams. These teams moved into my organization after facing cultural setbacks, program cancellations, or bad management. Through listening, building psychological safety, providing opportunities, identifying future leaders, and refusing egos, I helped turn them around. 

Helping teams become self-sufficient has defined my goals and career in senior management. That led to opportunities at Google where I could use those skills and my engineering experience.

In what ways have you personally navigated the intersection of diversity, equity, and inclusion (DEI) with technology throughout your career?

Personally, as a Sikh, I rarely see people who look like me in my city, let alone in my industry.  At times, I have felt alone. I’ve asked myself, what will colleagues think and see the first time we meet?

I’ve been aware of representing my community well, so nobody holds a bias against those who come after me. I feel the need to prove my community, not just myself, while feeling grateful for the Sikhs who broke barriers, so I didn’t have to be the first. When I started looking for internships, I considered changing my name. When I first worked on the Hololens, I couldn’t wear it over my turban.

These experiences led me to want to create a representative workplace that focuses on what you can do rather than what you look like or where you came from. A workplace that lets you be your authentic self. A workplace where you create products for everyone.

Given your experience, what personal strategies or approaches have you found effective in promoting diversity within tech teams and ensuring equitable outcomes?

One lesson I received early in my career in ensuring our recruiting pipeline was more representative was patience. One of my former general managers shared a statistic or a rule of halves:

• 32 applications submitted
• 16 resumes reviewed by the hiring manager
• 8 candidates interviewed over an initial phone screen
• 4 candidates in final onsite interviews
• 2 offers given
• 1 offer accepted

His point was that if you review applications in order, you will likely find a suitable candidate in the first thirty applications. To ensure you have a representative pipeline, you have to leave the role open to accept more applications, and you get to decide which applications to review first. 

Additionally, when creating job requisitions, prioritize what’s important for the company and not just the job. What are the skills and requirements in the long term? What skills are only necessary for the short term? I like to say, don’t just hire the best person for the job today, hire the best person for the team for the next five years. Try to screen in instead of screening out.

To ensure equitable outcomes, I point to my second leadership value, equity in opportunity. The reality of any team is that there might be limited high-visibility opportunities at any given time. For my teams, no matter how well someone delivered in the past, the next opportunity and challenge are given to someone else. Even if others might complete it faster, everyone deserves a chance to learn and grow. 

Moreover, we can focus on moving far, not just fast, when everyone grows. When this is practiced and rewarded, teams often find themselves being patient and supporting those currently leading efforts. While I don’t fault individuals who disagree, their growth isn’t more important than the team’s.

From your perspective, what advice would you offer to tech leaders and organizations looking to strengthen their DEI initiatives, particularly in the context of developing and deploying AI technologies?

My first advice for any DEI initiative is to be patient. You won’t see changes in one day, so you want to focus on seeing changes over time. That means not giving up early, with leaders providing their teams more time to recruit and interview rather than threatening position clawbacks if the vacancy isn’t filled.

Ultimately, AI models are only as good as the data they are trained on. Leaders need to think about the quality of the data. Do they have enough? Is there bias? Is there data that might help remove human biases? 

How do biased AI models perpetuate diversity disparities in hiring processes, and what role do diverse perspectives play in mitigating these biases in AI development?

Companies that already lack representation risk training their AI models on the skewed data of their current workforce. For example, among several outlets, Harvard Business Review has reported that women might only apply to a job if they have 100% of the required skills compared to men who apply when they meet just 60% of the skills. Suppose a company’s model was built on the skills and qualifications of their existing employees, some of which might not even be relevant to the role. In that case, it might discourage or screen out qualified candidates who don’t possess the same skillset.

Organizations should absolutely use data from current top performers but should be careful not to include irrelevant data. For example, how employees answer specific interview questions and perform actual work-related tasks is more relevant than their alma mater. They can fine-tune this model to give extra weight to data for underrepresented high performers in their organization. This change will open up the pipeline to a much broader population because the model looks at the skills that matter.

In your view, how can AI technologies be leveraged to enhance, rather than hinder, diversity and inclusion efforts within tech organizations?

Many organizations already have inherent familiarity biases. For example, they might prefer recruiting from the same universities or companies year after year. While it’s important to acknowledge that bias, it’s also important to remember that recruiting is challenging and competitive, and those avenues have likely consistently yielded candidates with less effort.

However, if organizations want to recruit better candidates, it makes sense to broaden their recruiting pool and leverage AI to make this more efficient. Traditionally, broadening the pool meant more effort in selecting a good candidate. But if you step back and focus on the skills that matter, you can develop various models to make recruiting easier. 

For example, biasing the model towards the traditional schools you recruit from doesn’t provide new value. However, if you collect data on successful employees and how they operate and solve problems, you could develop a model that helps interview candidates to determine their relevant skills. This doesn’t just help open doors to new candidates and create new pipelines, but strengthens the quality of recruiting from existing pipelines.

Then again, reinforcing the same skills could remove candidates with unique talent and out-of-the-box ideas that your organization doesn’t know it needs yet. The strategy above doesn’t necessarily promote diversity in thought.

As with any model, one must be careful to really know and understand what problem you’re solving and what success looks like, and that must be without bias.

In what specific ways do you believe AI can be utilized to identify and address systemic barriers to gender equality and diversity in tech careers?

When we know what data to collect and what data matters, we understand where we introduce bias, place less effort, and miss gaps. For example, the HBR study I shared that indicated women needed 100% of the skills to apply also debunked the idea that confidence was the deciding factor. Men and women cited confidence as the reason not to apply equally. The reality was that people needed to familiarize themselves with the hiring process and what skills were considered. So our understanding and biases come into play even when trying to remove bias!

An example I often use for AI is medical imaging. A radiologist regularly looks at MRIs. However, their ability to detect an anomaly could be affected by multiple factors. Are they distracted or tired? Are they in a rush? While AI models may have other issues, they aren’t susceptible to these factors. Moreover, continuous training of AI models means revisiting previous images and diagnoses to improve further because time isn’t a limitation. 

I share this example because humans make mistakes and form biases. Our judgment can be clouded on a specific day. If we focus on ensuring these models don’t inherit our biases, then we remove human judgment and error from the equation. This will ideally lead to hiring the mythical “best” candidate objectively and not subjectively.

As we conclude, what are your thoughts on the future of AI in relation to diversity and inclusion efforts within the tech sector? What key trends or developments do you foresee in the coming years?

I am optimistic that a broader population will have access to opportunities that focus on their skills and abilities versus their background and that there will be less bias when evaluating those skills. At the same time, I predict a bumpy road. 

Teams will need to reevaluate what’s important to perform the job and what’s helpful for the company, and that’s not always easy to do without bias. My hope is that in an economy of urgency, we are patient in how we approach improving representation and that we are willing to iterate rather than give up.

Kiranbir Sodhia

Senior Staff Engineering Manager at Google

Kiranbir Sodhia, a distinguished leader and engineer in Silicon Valley, California, has spent over 15 years at the cutting edge of AI, AR, gaming, mobile app, and semiconductor industries. His expertise extends beyond product innovation to transforming tech teams within top companies. At Microsoft, he revitalized two key organizations, consistently achieving top workgroup health scores from 2017 to 2022, and similarly turned around two teams at Google, where he also successfully mentored leaders for succession. Kiranbir ‘s leadership is characterized by a focus on fixing cultural issues, nurturing talent, and fostering strategic independence, with a mission to empower teams to operate independently and thrive. Kiranbir Sodhia: Transforming Tech Teams; Cultivating Leaders

The post AITech Interview with Kiranbir Sodhia, Senior Staff Engineering Manager at Google first appeared on AI-Tech Park.

It must be noted that the existence of lakehouse architectures has brought some substantial changes in the data architecture landscape. In this evolution process, organizations are still struggling on how to handle complex and diverse data management, to which the answer is the lakehouse model. Lakehouses can be viewed as a better integration of data lakes and data warehouses to provide improved data management systems. This blog post delves into the further evolution of lakehouse architecture and explains its main concepts, recent developments, and transformation of today’s data management.

1. Historical context and core principles

Before understanding the progression of architectural styles of the lakehouse, it is crucial to look at the basic components of the concept. Earlier, companies used data warehouses for structured data processing and analysis. Data warehouses offered strong and well-developed SQLQuery, transactional, and near real-time query processing for complicated queries. However, it became a drawback when attempting to work with different and more complex types of data that are incompatible with the one-dimensional, rigid structure of a regular list.

On the other hand, data lakes are a concept that appeared as a result of these limitations, allowing managing raw and unstructured information in a big data environment. Data lakes allowed for accepting and storing data in various formats from different sources; however, they did not offer the usage of atomicity, consistency, isolation, and durability (ACID) transactions and performance improvements typical for data warehouses.

Consequently, the architecture of the lakehouse strived to combine these two paradigms into an integrated system that would represent the advantages of both. To summarize, lakehouses are the next step in data organization with their combination of data lake scalability and flexibility and data warehouse performance and control.

2. Key Advancements in Lakehouse Architecture

2.1 Unified Storage and Compute Layer:

The lakehouse architecture brings in a simplified storage and compute layer in their architectural design, thus minimizing the level of complexity. This layer enables organizations to archive data while fulfilling many types of data processing duties, from batch to real-time. The decoupling of compute and storage resources is a great improvement in regards to scale efficiency.

2.2 Transactional Capabilities and ACID Compliance:

One of the more substantial changes included in the contemporary architecture of the lakehouse is transactionality and ACID compliance. It guarantees the durability and reliability of the data operations, which solves one of the major weaknesses of the data lakes. At the same time, the application of these transactional features will allow the lakehouse to work with large amounts of data and perform complex calculations without affecting the quality of information.

2.3 Advanced Metadata Management:

Another area where some advances have been registered in the area of lakehouse architectures refers to metadata management as a critical area in the governance and discoverability of the available data. Today’s Lakehouse provides complex metadata directories that help in data indexing, lineage, and schema change tracking. These innovations help the user to search for data as well as look into slices of it and thus make operations more productive.

2.4 Support for Diverse Data Types and Workloads:

Other improvements in the development of lakehouse architecture are related to expanded support of various features and contributors of a dataset. This flexibility enables organizations to do not only the normal SQL query analysis work but also the higher-end machine learning and artificial intelligence-related work. Consequently, lakehouses’ capability to support structure, semi-structured, and unstructured data places them as ideal platforms for complex analysis.

2.5 Enhanced Data Security and Governance:

The protection and management of data continue to be crucial concerns in organizations. Lakehouse architectures embrace a range of security measures such as high-level access control, data encryption, and audit functions. These features provide a means of guarding data against unauthorized access and leakage and compiling with the laid-down regulations.

3. Implications for Modern Data Management

The concept of Lakehouse brings the best of architecture to manage data on newer frontiers and adopts it to enhance the existing data management. Thus, the concept of lakehouses provides a single framework for processing multiple classes of data tasks, thus improving the efficiency of an organization’s work with data assets. The real-time data processing and strong transactional foundations also give organizations the confidence to make decisions based on their data.

Also, better metadata management and supporting security options in a lakehouse enhance overall data governance and compliance. Consequently, organizations are in a peculiar position of being able to manage their data resources in a similar way so that when quality and accuracy as well as regulatory compliance are under consideration, it can easily be achieved.

As organizations grow in stature and face the challenges of handling data more efficiently, the concepts of data management bring the concept of lakehouse architecture as something that solves the problems with traditional data systems. The combination of strengths of a data lake and a data warehouse makes the solution of a lakehouse very strong and versatile for today’s complex data scenarios.

4. Conclusion

The concept of lakehouse architecture is one of the most significant steps toward improving data handling processes. Lakehouses, on the other hand, offer a combined approach to data lakes and data warehouses that improves scalability, performance, and governance. When employing this innovative architecture, organizations prepare themselves to get the most out of the gathered data, to foster analysis and creativity in a world headed towards a higher dependency on data and information.

Explore AITechPark for top AI, IoT, Cybersecurity advancements, And amplify your reach through guest posts and link collaboration.

The post The Evolution of Lakehouse Architecture first appeared on AI-Tech Park.