#AI ML Training Data Company

[dataseeders]

Training Data for AI ML Models

We specialize in providing cutting-edge training data solutions for Artificial Intelligence (AI) and Machine Learning (ML) applications. Contact us today to explore how our precision training data can elevate the performance of your AI and ML projects.

Know More - AI ML Training Data

[apexcovantage]

Generative AI | High-Quality Human Expert Labeling | Apex Data Sciences

Apex Data Sciences combines cutting-edge generative AI with RLHF for superior data labeling solutions. Get high-quality labeled data for your AI projects.

[connectinfosofttechnologies]

Machine Learning And IoT: How It Can Be Beneficial For Businesses?

The combination of machine learning and IoT presents significant benefits for businesses. By leveraging machine learning algorithms on IoT devices, businesses can extract valuable insights from the vast amount of data generated by these interconnected devices. This allows for real-time analysis, predictive modeling, and intelligent decision-making. Machine learning can help optimize processes, detect anomalies, and improve efficiency within IoT systems. 

[catalyswitch]

Was talking with wife recently about AI and the ways it's incredibly stupid and I am reminded of the time a few years ago the Execs at the place I worked previously wanted to incorporate AI into our workflow in order to help materials development. They wanted to make sure that the company was "utilizing the latest technology to make us more productive" so they partnered with a company that uses AI/ML to predict chemical structures in order to enhance performance based on our desired properties. My boss and I kinda thought this was stupid when it was first announced, but we were still unprepared for how bad it was really going to be.

The problem of course here is that what a computer thinks is good and will perform well does not often make sense according to the laws of physics. So more often than not the computer would spit out extremely specific and nonsensical structures that it believed would increase performance. These structures could range from completely impractical to sometimes downright impossible to actually make, so for every set of predictions we got back we had to first filter all the nonsense and then select a set from the ones that could be made and tested in a reasonable amount of time. In addition, they emphasized that the more data that they have the better the predictions would be, so the pressure was on to synthesize and validate as many molecules as possible as quickly as possible. This was a huge drain on time and energy because again some of these structures were nontrivial to make. Not that the computer people would be able to tell the difference. But still the executives were excited about it so we gave it a try anyway. The idea was that we would start by making a bunch of different materials and test the results and then feed those results back into the machine to predict better structures based on the ever growing data pool.

The funny part of the story, of course, is that with every iteration, the performance got worse. This was not surprising to me. The mechanisms that dictate performance in this field are not fully understood even now, and there are still many papers coming out every year adding more knowledge to the field. Additionally, the predictions weren't being made using some fundamental understanding of the mechanisms at play, but by training an algorithm using a pool of existing literature. You're just not going to get good results by "midjourneying" chemistry. We did around 3-4 iteration cycles with them over that year contract and every time the performance of the structures that it had predicted were worse than the last set, sometimes dramatically so. And they would tell us "no no, the data set isn't really big enough to give good results yet" and "once the model has tested enough structures it'll get better" but it didn't in that period. And it's possible that on a long enough timescale it might be possible? But, the reality was that we had a whole year of time and resources essentially wasted because our CEO thought that some tech guys in SV could use AI to do chemistry and didn't believe us when we said it was stupid.

And you know what? We figured out something that worked really well less than six months after dumping them and getting to do it our way again.

[elwenyere]

I saw a post the other day calling criticism of generative AI a moral panic, and while I do think many proprietary AI technologies are being used in deeply unethical ways, I think there is a substantial body of reporting and research on the real-world impacts of the AI boom that would trouble the comparison to a moral panic: while there *are* older cultural fears tied to negative reactions to the perceived newness of AI, many of those warnings are Luddite with a capital L - that is, they're part of a tradition of materialist critique focused on the way the technology is being deployed in the political economy. So (1) starting with the acknowledgement that a variety of machine-learning technologies were being used by researchers before the current "AI" hype cycle, and that there's evidence for the benefit of targeted use of AI techs in settings where they can be used by trained readers - say, spotting patterns in radiology scans - and (2) setting aside the fact that current proprietary LLMs in particular are largely bullshit machines, in that they confidently generate errors, incorrect citations, and falsehoods in ways humans may be less likely to detect than conventional disinformation, and (3) setting aside as well the potential impact of frequent offloading on human cognition and of widespread AI slop on our understanding of human creativity...

What are some of the material effects of the "AI" boom?

Guzzling water and electricity

The data centers needed to support AI technologies require large quantities of water to cool the processors. A to-be-released paper from the University of California Riverside and the University of Texas Arlington finds, for example, that "ChatGPT needs to 'drink' [the equivalent of] a 500 ml bottle of water for a simple conversation of roughly 20-50 questions and answers." Many of these data centers pull water from already water-stressed areas, and the processing needs of big tech companies are expanding rapidly. Microsoft alone increased its water consumption from 4,196,461 cubic meters in 2020 to 7,843,744 cubic meters in 2023. AI applications are also 100 to 1,000 times more computationally intensive than regular search functions, and as a result the electricity needs of data centers are overwhelming local power grids, and many tech giants are abandoning or delaying their plans to become carbon neutral. Google’s greenhouse gas emissions alone have increased at least 48% since 2019. And a recent analysis from The Guardian suggests the actual AI-related increase in resource use by big tech companies may be up to 662%, or 7.62 times, higher than they've officially reported.

Exploiting labor to create its datasets

Like so many other forms of "automation," generative AI technologies actually require loads of human labor to do things like tag millions of images to train computer vision for ImageNet and to filter the texts used to train LLMs to make them less racist, sexist, and homophobic. This work is deeply casualized, underpaid, and often psychologically harmful. It profits from and re-entrenches a stratified global labor market: many of the data workers used to maintain training sets are from the Global South, and one of the platforms used to buy their work is literally called the Mechanical Turk, owned by Amazon.

From an open letter written by content moderators and AI workers in Kenya to Biden: "US Big Tech companies are systemically abusing and exploiting African workers. In Kenya, these US companies are undermining the local labor laws, the country’s justice system and violating international labor standards. Our working conditions amount to modern day slavery."

Deskilling labor and demoralizing workers

The companies, hospitals, production studios, and academic institutions that have signed contracts with providers of proprietary AI have used those technologies to erode labor protections and worsen working conditions for their employees. Even when AI is not used directly to replace human workers, it is deployed as a tool for disciplining labor by deskilling the work humans perform: in other words, employers use AI tech to reduce the value of human labor (labor like grading student papers, providing customer service, consulting with patients, etc.) in order to enable the automation of previously skilled tasks. Deskilling makes it easier for companies and institutions to casualize and gigify what were previously more secure positions. It reduces pay and bargaining power for workers, forcing them into new gigs as adjuncts for its own technologies.

I can't say anything better than Tressie McMillan Cottom, so let me quote her recent piece at length: "A.I. may be a mid technology with limited use cases to justify its financial and environmental costs. But it is a stellar tool for demoralizing workers who can, in the blink of a digital eye, be categorized as waste. Whatever A.I. has the potential to become, in this political environment it is most powerful when it is aimed at demoralizing workers. This sort of mid tech would, in a perfect world, go the way of classroom TVs and MOOCs. It would find its niche, mildly reshape the way white-collar workers work and Americans would mostly forget about its promise to transform our lives. But we now live in a world where political might makes right. DOGE’s monthslong infomercial for A.I. reveals the difference that power can make to a mid technology. It does not have to be transformative to change how we live and work. In the wrong hands, mid tech is an antilabor hammer."

Enclosing knowledge production and destroying open access

OpenAI started as a non-profit, but it has now become one of the most aggressive for-profit companies in Silicon Valley. Alongside the new proprietary AIs developed by Google, Microsoft, Amazon, Meta, X, etc., OpenAI is extracting personal data and scraping copyrighted works to amass the data it needs to train their bots - even offering one-time payouts to authors to buy the rights to frack their work for AI grist - and then (or so they tell investors) they plan to sell the products back at a profit. As many critics have pointed out, proprietary AI thus works on a model of political economy similar to the 15th-19th-century capitalist project of enclosing what was formerly "the commons," or public land, to turn it into private property for the bourgeois class, who then owned the means of agricultural and industrial production. "Open"AI is built on and requires access to collective knowledge and public archives to run, but its promise to investors (the one they use to attract capital) is that it will enclose the profits generated from that knowledge for private gain.

AI companies hungry for good data to train their Large Language Models (LLMs) have also unleashed a new wave of bots that are stretching the digital infrastructure of open-access sites like Wikipedia, Project Gutenberg, and Internet Archive past capacity. As Eric Hellman writes in a recent blog post, these bots "use as many connections as you have room for. If you add capacity, they just ramp up their requests." In the process of scraping the intellectual commons, they're also trampling and trashing its benefits for truly public use.

Enriching tech oligarchs and fueling military imperialism

The names of many of the people and groups who get richer by generating speculative buzz for generative AI - Elon Musk, Mark Zuckerberg, Sam Altman, Larry Ellison - are familiar to the public because those people are currently using their wealth to purchase political influence and to win access to public resources. And it's looking increasingly likely that this political interference is motivated by the probability that the AI hype is a bubble - that the tech can never be made profitable or useful - and that tech oligarchs are hoping to keep it afloat as a speculation scheme through an infusion of public money - a.k.a. an AIG-style bailout.

In the meantime, these companies have found a growing interest from military buyers for their tech, as AI becomes a new front for "national security" imperialist growth wars. From an email written by Microsoft employee Ibtihal Aboussad, who interrupted Microsoft AI CEO Mustafa Suleyman at a live event to call him a war profiteer: "When I moved to AI Platform, I was excited to contribute to cutting-edge AI technology and its applications for the good of humanity: accessibility products, translation services, and tools to 'empower every human and organization to achieve more.' I was not informed that Microsoft would sell my work to the Israeli military and government, with the purpose of spying on and murdering journalists, doctors, aid workers, and entire civilian families. If I knew my work on transcription scenarios would help spy on and transcribe phone calls to better target Palestinians, I would not have joined this organization and contributed to genocide. I did not sign up to write code that violates human rights."

So there's a brief, non-exhaustive digest of some vectors for a critique of proprietary AI's role in the political economy. tl;dr: the first questions of material analysis are "who labors?" and "who profits/to whom does the value of that labor accrue?"

For further (and longer) reading, check out Justin Joque's Revolutionary Mathematics: Artificial Intelligence, Statistics and the Logic of Capitalism and Karen Hao's forthcoming Empire of AI.

[govindhtech]

Benefits Of Conversational AI & How It Works With Examples

What Is Conversational AI?

Conversational AI mimics human speech. It’s made possible by Google’s foundation models, which underlie new generative AI capabilities, and NLP, which helps computers understand and interpret human language.

How Conversational AI works

Natural language processing (NLP), foundation models, and machine learning (ML) are all used in conversational AI.

Large volumes of speech and text data are used to train conversational AI systems. The machine is trained to comprehend and analyze human language using this data. The machine then engages in normal human interaction using this information. Over time, it improves the quality of its responses by continuously learning from its interactions.

Conversational AI For Customer Service

With IBM Watsonx Assistant, a next-generation conversational AI solution, anyone in your company can easily create generative AI assistants that provide customers with frictionless self-service experiences across all devices and channels, increase employee productivity, and expand your company.

User-friendly: Easy-to-use UI including pre-made themes and a drag-and-drop chat builder.

Out-of-the-box: Unconventional To better comprehend the context of each natural language communication, use large language models, large speech models, intelligent context gathering, and natural language processing and understanding (NLP, NLU).

Retrieval-augmented generation (RAG): It based on your company’s knowledge base, provides conversational responses that are correct, relevant, and current at all times.

Use cases

Watsonx Assistant may be easily set up to accommodate your department’s unique requirements.

Customer service

Strong client support With quick and precise responses, chatbots boost sales while saving contact center funds.

Human resources

All of your employees may save time and have a better work experience with HR automation. Questions can be answered by staff members at any time.

Marketing

With quick, individualized customer service, powerful AI chatbot marketing software lets you increase lead generation and enhance client experiences.

Features

Examine ways to increase production, enhance customer communications, and increase your bottom line.

Artificial Intelligence

Strong Watsonx Large Language Models (LLMs) that are tailored for specific commercial applications.

The Visual Builder

Building generative AI assistants using to user-friendly interface doesn’t require any coding knowledge.

Integrations

Pre-established links with a large number of channels, third-party apps, and corporate systems.

Security

Additional protection to prevent hackers and improper use of consumer information.

Analytics

Comprehensive reports and a strong analytics dashboard to monitor the effectiveness of conversations.

Self-service accessibility

For a consistent client experience, intelligent virtual assistants offer self-service responses and activities during off-peak hours.

Benfits of Conversational AI

Automation may save expenses while boosting output and operational effectiveness.

Conversational AI, for instance, may minimize human error and expenses by automating operations that are presently completed by people. Increase client happiness and engagement by providing a better customer experience.

Conversational AI, for instance, may offer a more engaging and customized experience by remembering client preferences and assisting consumers around-the-clock when human agents are not present.

Conversational AI Examples

Here are some instances of conversational AI technology in action:

Virtual agents that employ generative AI to support voice or text conversations are known as generative AI agents.

Chatbots are frequently utilized in customer care applications to respond to inquiries and offer assistance.

Virtual assistants are frequently voice-activated and compatible with smart speakers and mobile devices.

Software that converts text to speech is used to produce spoken instructions or audiobooks.

Software for speech recognition is used to transcribe phone conversations, lectures, subtitles, and more.

Applications Of Conversational AI

Customer service: Virtual assistants and chatbots may solve problems, respond to frequently asked questions, and offer product details.

E-commerce: Chatbots driven by AI can help customers make judgments about what to buy and propose products.

Healthcare: Virtual health assistants are able to make appointments, check patient health, and offer medical advice.

Education: AI-powered tutors may respond to student inquiries and offer individualized learning experiences.

In summary

The way to communicate with robots might be completely changed by the formidable technology known as conversational AI. Also can use its potential to produce more effective, interesting, and customized experiences if it comprehend its essential elements, advantages, and uses.

Read more on Govindhech.com

[christianbale121]

AI Agent Development: How to Create Intelligent Virtual Assistants for Business Success

In today's digital landscape, businesses are increasingly turning to AI-powered virtual assistants to streamline operations, enhance customer service, and boost productivity. AI agent development is at the forefront of this transformation, enabling companies to create intelligent, responsive, and highly efficient virtual assistants. In this blog, we will explore how to develop AI agents and leverage them for business success.

Understanding AI Agents and Virtual Assistants

AI agents, or intelligent virtual assistants, are software programs that use artificial intelligence, machine learning, and natural language processing (NLP) to interact with users, automate tasks, and make decisions. These agents can be deployed across various platforms, including websites, mobile apps, and messaging applications, to improve customer engagement and operational efficiency.

Key Features of AI Agents

Natural Language Processing (NLP): Enables the assistant to understand and process human language.

Machine Learning (ML): Allows the assistant to improve over time based on user interactions.

Conversational AI: Facilitates human-like interactions.

Task Automation: Handles repetitive tasks like answering FAQs, scheduling appointments, and processing orders.

Integration Capabilities: Connects with CRM, ERP, and other business tools for seamless operations.

Steps to Develop an AI Virtual Assistant

1. Define Business Objectives

Before developing an AI agent, it is crucial to identify the business goals it will serve. Whether it's improving customer support, automating sales inquiries, or handling HR tasks, a well-defined purpose ensures the assistant aligns with organizational needs.

2. Choose the Right AI Technologies

Selecting the right technology stack is essential for building a powerful AI agent. Key technologies include:

NLP frameworks: OpenAI's GPT, Google's Dialogflow, or Rasa.

Machine Learning Platforms: TensorFlow, PyTorch, or Scikit-learn.

Speech Recognition: Amazon Lex, IBM Watson, or Microsoft Azure Speech.

Cloud Services: AWS, Google Cloud, or Microsoft Azure.

3. Design the Conversation Flow

A well-structured conversation flow is crucial for user experience. Define intents (what the user wants) and responses to ensure the AI assistant provides accurate and helpful information. Tools like chatbot builders or decision trees help streamline this process.

4. Train the AI Model

Training an AI assistant involves feeding it with relevant datasets to improve accuracy. This may include:

Supervised Learning: Using labeled datasets for training.

Reinforcement Learning: Allowing the assistant to learn from interactions.

Continuous Learning: Updating models based on user feedback and new data.

5. Test and Optimize

Before deployment, rigorous testing is essential to refine the AI assistant's performance. Conduct:

User Testing: To evaluate usability and responsiveness.

A/B Testing: To compare different versions for effectiveness.

Performance Analysis: To measure speed, accuracy, and reliability.

6. Deploy and Monitor

Once the AI assistant is live, continuous monitoring and optimization are necessary to enhance user experience. Use analytics to track interactions, identify issues, and implement improvements over time.

Benefits of AI Virtual Assistants for Businesses

1. Enhanced Customer Service

AI-powered virtual assistants provide 24/7 support, instantly responding to customer queries and reducing response times.

2. Increased Efficiency

By automating repetitive tasks, businesses can save time and resources, allowing employees to focus on higher-value tasks.

3. Cost Savings

AI assistants reduce the need for large customer support teams, leading to significant cost reductions.

4. Scalability

Unlike human agents, AI assistants can handle multiple conversations simultaneously, making them highly scalable solutions.

5. Data-Driven Insights

AI assistants gather valuable data on customer behavior and preferences, enabling businesses to make informed decisions.

Future Trends in AI Agent Development

1. Hyper-Personalization

AI assistants will leverage deep learning to offer more personalized interactions based on user history and preferences.

2. Voice and Multimodal AI

The integration of voice recognition and visual processing will make AI assistants more interactive and intuitive.

3. Emotional AI

Advancements in AI will enable virtual assistants to detect and respond to human emotions for more empathetic interactions.

4. Autonomous AI Agents

Future AI agents will not only respond to queries but also proactively assist users by predicting their needs and taking independent actions.

Conclusion

AI agent development is transforming the way businesses interact with customers and streamline operations. By leveraging cutting-edge AI technologies, companies can create intelligent virtual assistants that enhance efficiency, reduce costs, and drive business success. As AI continues to evolve, embracing AI-powered assistants will be essential for staying competitive in the digital era.

[kaaylabs]

Optimizing Business Operations with Advanced Machine Learning Services

Machine learning has gained popularity in recent years thanks to the adoption of the technology. On the other hand, traditional machine learning necessitates managing data pipelines, robust server maintenance, and the creation of a model for machine learning from scratch, among other technical infrastructure management tasks. Many of these processes are automated by machine learning service which enables businesses to use a platform much more quickly.

What do you understand of Machine learning?

Deep learning and neural networks applied to data are examples of machine learning, a branch of artificial intelligence focused on data-driven learning. It begins with a dataset and gains the ability to extract relevant data from it.

Machine learning technologies facilitate computer vision, speech recognition, face identification, predictive analytics, and more. They also make regression more accurate.

For what purpose is it used?

Many use cases, such as churn avoidance and support ticket categorization make use of MLaaS. The vital thing about MLaaS is it makes it possible to delegate machine learning's laborious tasks. This implies that you won't need to install software, configure servers, maintain infrastructure, and other related tasks. All you have to do is choose the column to be predicted, connect the pertinent training data, and let the software do its magic.  

Natural Language Interpretation

By examining social media postings and the tone of consumer reviews, natural language processing aids businesses in better understanding their clientele. the ml services enable them to make more informed choices about selling their goods and services, including providing automated help or highlighting superior substitutes. Machine learning can categorize incoming customer inquiries into distinct groups, enabling businesses to allocate their resources and time.

Predicting

Another use of machine learning is forecasting, which allows businesses to project future occurrences based on existing data. For example, businesses that need to estimate the costs of their goods, services, or clients might utilize MLaaS for cost modelling.

Data Investigation

Investigating variables, examining correlations between variables, and displaying associations are all part of data exploration. Businesses may generate informed suggestions and contextualize vital data using machine learning.

Data Inconsistency

Another crucial component of machine learning is anomaly detection, which finds anomalous occurrences like fraud. This technology is especially helpful for businesses that lack the means or know-how to create their own systems for identifying anomalies.

Examining And Comprehending Datasets

Machine learning provides an alternative to manual dataset searching and comprehension by converting text searches into SQL queries using algorithms trained on millions of samples. Regression analysis use to determine the correlations between variables, such as those affecting sales and customer satisfaction from various product attributes or advertising channels.

Recognition Of Images

One area of machine learning that is very useful for mobile apps, security, and healthcare is image recognition. Businesses utilize recommendation engines to promote music or goods to consumers. While some companies have used picture recognition to create lucrative mobile applications.

Your understanding of AI will drastically shift. They used to believe that AI was only beyond the financial reach of large corporations. However, thanks to services anyone may now use this technology.

[elsa16744]

Healthcare Market Research: Why Does It Matter? 

Healthcare market research (MR) providers interact with several stakeholders to discover and learn about in-demand treatment strategies and patients’ requirements. Their insightful reports empower medical professionals, insurance companies, and pharma businesses to engage with patients in more fulfilling ways. This post will elaborate on the growing importance of healthcare market research. 

What is Healthcare Market Research? 

Market research describes consumer and competitor behaviors using first-hand or public data collection methods, like surveys and web scraping. In medicine and life sciences, clinicians and accessibility device developers can leverage it to improve patient outcomes. They grow faster by enhancing their approaches as validated MR reports recommend. 

Finding key opinion leaders (KOL), predicting demand dynamics, or evaluating brand recognition efforts becomes more manageable thanks to domain-relevant healthcare market research consulting. Although primary MR helps with authority-building, monitoring how others in the target field innovate their business models is also essential. So, global health and life science enterprises value secondary market research as much as primary data-gathering procedures. 

The Importance of Modern Healthcare Market Research 

1| Learning What Competitors Might Do Next 

Businesses must beware of market share fluctuations due to competitors’ expansion strategies. If your clients are more likely to seek help from rival brands, this situation suggests failure to compete. 

Companies might provide fitness products, over-the-counter (OTC) medicines, or childcare facilities. However, they will always lose to a competitor who can satisfy the stakeholders’ demands more efficiently. These developments evolve over the years, during which you can study and estimate business rivals’ future vision. 

You want to track competing businesses’ press releases, public announcements, new product launches, and marketing efforts. You must also analyze their quarter-on-quarter market performance. If the data processing scope exceeds your tech capabilities, consider using healthcare data management services offering competitive intelligence integrations. 

2| Understanding Patients and Their Needs for Unique Treatment  

Patients can experience unwanted bodily changes upon consuming a medicine improperly. Otherwise, they might struggle to use your accessibility technology. If healthcare providers implement a user-friendly feedback and complaint collection system, they can reduce delays. As a result, patients will find a cure for their discomfort more efficiently. 

However, processing descriptive responses through manual means is no longer necessary. Most market research teams have embraced automated unstructured data processing breakthroughs. They can guess a customer’s emotions and intentions from submitted texts without frequent human intervention. This era of machine learning (ML) offers ample opportunities to train ML systems to sort patients’ responses quickly. 

So, life science companies can increase their employees’ productivity if their healthcare market research providers support ML-based feedback sorting and automation strategies. 

Besides, hospitals, rehabilitation centers, and animal care facilities can incorporate virtual or physical robots powered by conversational artificial intelligence (AI). Doing so is one of the potential approaches to addressing certain patients’ loneliness problems throughout hospitalization. Utilize MR to ask your stakeholders whether such integrations improve their living standards. 

3| Improving Marketing and Sales 

Healthcare market research aids pharma and biotechnology corporations to categorize customer preferences according to their impact on sales. It also reveals how brands can appeal to more people when introducing a new product or service. One approach is to shut down or downscale poorly performing ideas. 

If a healthcare facility can reduce resources spent on underperforming promotions, it can redirect them to more engaging campaigns. Likewise, MR specialists let patients and doctors directly communicate their misgivings about such a medicine or treatment via online channels. The scale of these surveys can extend to national, continental, or global markets. It is more accessible as cloud platforms flexibly adjust the resources a market research project may need. 

With consistent communication involving doctors, patients, equipment vendors, and pharmaceutical brands, the healthcare industry will be more accountable. It will thrive sustainably. 

Healthcare Market Research: Is It Ethical? 

Market researchers in healthcare and life sciences will rely more on data-led planning as competition increases and customers demand richer experiences like telemedicine. Remember, it is not surprising how awareness regarding healthcare infrastructure has skyrocketed since 2020. At the same time, life science companies must proceed with caution when handling sensitive data in a patient’s clinical history. 

On one hand, universities and private research projects need more healthcare data. Meanwhile, threats of clinical record misuse are real, having irreparable financial and psychological damage potential. 

Ideally, hospitals, laboratories, and pharmaceutical firms must inform patients about the use of health records for research or treatment intervention. Today, reputed data providers often conduct MR surveys, use focus groups, and scan scholarly research publications. They want to respect patients’ choice in who gets to store, modify, and share the data. 

Best Practices for Healthcare Market Research Projects 

Legal requirements affecting healthcare data analysis, market research, finance, and ethics vary worldwide. Your data providers must recognize and respect this reality. Otherwise, gathering, storing, analyzing, sharing, or deleting a patient’s clinical records can increase legal risks. 

Even if a healthcare business has no malicious intention behind extracting insights, cybercriminals can steal healthcare data. Therefore, invest in robust IT infrastructure, partner with experts, and prioritize data governance. 

Like customer-centricity in commercial market research applications, dedicate your design philosophy to patient-centricity. 

Incorporating health economics and outcomes research (HEOR) will depend on real-world evidence (RWE). Therefore, protect data integrity and increase quality management standards. If required, find automated data validation assistance and develop or rent big data facilities. 

Capture data on present industry trends while maintaining a grasp on long-term objectives. After all, a lot of data is excellent for accuracy, but relevance is the backbone of analytical excellence and business focus. 

Conclusion 

Given this situation, transparency is the key to protecting stakeholder faith in healthcare data management. As such, MR consultants must act accordingly. Healthcare market research is not unethical. Yet, this statement stays valid only if a standardized framework specifies when patients’ consent trumps medical researchers’ data requirements. Healthcare market research is not unethical. Yet, this statement stays valid only if a standardized framework specifies when patients’ consent trumps medical researchers’ data requirements. 

Market research techniques can help fix the long-standing communication and ethics issues in doctor-patient relationships if appropriately configured, highlighting their importance in the healthcare industry’s progress. When patients willingly cooperate with MR specialists, identifying recovery challenges or clinical devices’ ergonomic failures is quick. No wonder that health and life sciences organizations want to optimize their offerings by using market research. 

[tubetrading]

ERP Trends 2024:  What Engineering and Manufacturing Industries Need to Know

As we navigate through 2024, the landscape of Enterprise Resource Planning (ERP) systems continues to evolve, presenting both opportunities and challenges for engineering and manufacturing industries.  Companies in this sector, especially those in key industrial regions like Maharashtra, Mumbai, Pune, and Gujarat, must stay abreast of the latest ERP trends to maintain competitive advantage and operational efficiency.  In this blog, we’ll delve into the significant ERP trends of 2024 and their implications for the engineering and manufacturing sectors.

1.    Increased Adoption of Cloud-Based ERP Solutions

One of the most significant trends in ERP software for engineering companies in Maharashtra and across India is the shift towards cloud-based solutions.  Cloud ERP offers several advantages over traditional on-premise systems, including lower upfront costs, greater scalability, and enhanced accessibility.

Benefits of Cloud-Based ERP:

Cost Efficiency:  Eliminates the need for expensive hardware and reduces IT maintenance costs.

Scalability:  Easily adjusts to the growing needs of a manufacturing company in Gujarat or an engineering firm in Mumbai.

Accessibility:  Provides access to real-time data from anywhere, facilitating better decision-making.

Leading ERP software companies in Pune are increasingly offering cloud-based solutions tailored to the needs of local engineering and manufacturing businesses.  These solutions support remote work and ensure business continuity in an increasingly digital world.

2.    Integration of AI and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing ERP systems by providing intelligent insights and automating routine tasks.  For ERP software for engineering companies in Mumbai, integrating AI can enhance predictive maintenance, optimize supply chain management, and improve production planning.

AI and ML Applications in ERP:

Predictive Analytics:  Helps foresee equipment failures and reduce downtime.

Supply Chain Optimization:  Enhances demand forecasting and inventory management.

Process Automation:  Automates repetitive tasks, freeing up human resources for more strategic roles.

ERP solution providers in Pune are at the forefront of integrating AI and ML into their systems, enabling companies to leverage advanced analytics and improve overall operational efficiency.

3.    Emphasis on Cybersecurity

With the increasing digitization of manufacturing processes, cybersecurity has become a critical concern.  ERP systems, being the backbone of business operations, are prime targets for cyber-attacks.  Engineering and manufacturing companies in regions like Gujarat and Maharashtra need robust cybersecurity measures to protect their sensitive data.

Key Cybersecurity Features:

Data Encryption:  Protects data during transmission and storage.

Multi-Factor Authentication:  Enhances user authentication processes.

Regular Security Audits:  Ensures continuous monitoring and improvement of security protocols.

ERP software for engineering companies in Maharashtra must incorporate these advanced security features to safeguard against data breaches and cyber threats.

4.    Enhanced User Experience (UX)

User experience has become a critical factor in ERP adoption and utilization.  Modern ERP systems are focusing on intuitive interfaces and user-friendly designs to ensure that all employees, regardless of their technical expertise, can effectively use the system.

UX Improvements:

Intuitive Dashboards:  Provide real-time insights and easy navigation.

Mobile Accessibility:  Ensures that users can access ERP data on-the-go.

Customization Options:  Allow users to tailor the system to their specific needs.

ERP software companies in Pune are prioritizing user experience in their solutions, making it easier for engineering and manufacturing firms to train their staff and increase productivity.

5.    Internet of Things (IoT) Integration

The integration of IoT with ERP systems is another trend transforming the manufacturing industry.  IoT devices collect vast amounts of data from production lines, equipment, and other operational areas, which can be analyzed by the ERP system to optimize performance.

IoT Benefits in ERP:

Real-Time Monitoring:  Provides immediate insights into production processes.

Predictive Maintenance:  Schedules maintenance activities based on equipment condition rather than time intervals.

Enhanced Quality Control:  Monitors product quality throughout the manufacturing process.

For ERP for manufacturing companies in Gujarat, IoT integration offers a significant advantage by improving efficiency and reducing operational costs.

6.    Sustainability and Green Manufacturing

Sustainability is becoming a critical focus for manufacturing companies worldwide.  ERP systems are evolving to support green manufacturing practices by tracking and optimizing resource usage, reducing waste, and ensuring compliance with environmental regulations.

Sustainable ERP Features:

Resource Management:  Tracks energy and material usage to minimize waste.

Regulatory Compliance:  Ensures adherence to environmental laws and standards.

Sustainability Reporting:  Provides detailed reports on sustainability metrics.

Engineering and manufacturing companies in regions like Mumbai and Maharashtra can benefit from ERP solutions that incorporate sustainability features, helping them achieve their environmental goals and enhance their corporate reputation.

7.    Modular and Flexible ERP Solutions

In response to the diverse needs of engineering and manufacturing firms, ERP solution providers in Pune are developing more modular and flexible ERP systems.  These systems allow companies to select and implement only the modules they need, which can be easily scaled and customized as their business grows.

Advantages of Modular ERP:

Cost-Effective:  Pay only for the features you need.

Scalability:  Easily add new modules as your business requirements evolve.

Customization:  Tailor the system to fit specific operational needs.

This trend is particularly beneficial for small to medium-sized enterprises (SMEs) in the engineering and manufacturing sectors, enabling them to adopt ERP systems without the burden of high costs or complexity.

8.    Focus on Customer-Centric Manufacturing

ERP systems are increasingly supporting customer-centric manufacturing practices, where production processes are aligned with customer needs and preferences.  This approach enhances customer satisfaction and drives business growth.

Customer-Centric ERP Features:

Custom Order Management:  Handles unique customer requirements and specifications.

Enhanced CRM Integration:  Integrates with customer relationship management (CRM) systems for a holistic view of customer interactions.

Real-Time Order Tracking:  Provides customers with real-time updates on their orders.

Manufacturing companies in Gujarat and engineering firms in Maharashtra are leveraging these customer-centric ERP features to improve their service levels and build stronger customer relationships.

9.    Advanced Analytics and Business Intelligence (BI)

Advanced analytics and BI are becoming integral components of modern ERP systems.  These tools provide deep insights into business operations, helping companies make data-driven decisions and improve performance.

Key BI Features:

Data Visualization:  Converts complex data into easy-to-understand charts and graphs.

Dashboards:  Offer a real-time overview of key performance indicators (KPIs).

Predictive Analytics:  Forecasts future trends based on historical data.

ERP software companies in Pune are incorporating advanced analytics and BI capabilities into their systems, empowering engineering and manufacturing firms to gain a competitive edge through better insights and informed decision-making.

10.  Globalization and Localization Support

As engineering and manufacturing companies expand their operations globally, ERP systems must support multiple languages, currencies, and regulatory requirements.  Globalization and localization features are essential for companies operating in diverse markets.

Globalization Features:

Multi-Language Support:  Accommodates users from different regions.

Multi-Currency Handling:  Manages transactions in various currencies.

Compliance with Local Regulations:  Ensures adherence to regional laws and standards.

ERP solution providers in Pune and other industrial hubs are enhancing their systems to support global operations, enabling companies to seamlessly manage their international business processes.

Conclusion

The ERP landscape for engineering and manufacturing industries is rapidly evolving, driven by advancements in technology and changing business needs.  Companies in Maharashtra, Mumbai, Pune, and Gujarat must stay informed about these trends to leverage the full potential of ERP systems.  By adopting cloud-based solutions, integrating AI and IoT, prioritizing cybersecurity, and focusing on sustainability, businesses can achieve greater efficiency, competitiveness, and growth in 2024 and beyond.

For engineering and manufacturing firms looking for the best ERP software for engineering companies in Maharashtra or ERP for manufacturing companies in Gujarat, it is crucial to partner with leading ERP solution providers in Pune who understand the unique challenges and opportunities in this sector.  Embracing these trends will not only enhance operational efficiency but also drive innovation and sustainability in the engineering and manufacturing industries.

By staying ahead of these ERP trends, companies can position themselves for success in an increasingly digital and interconnected world.  Whether you are an engineering firm in Mumbai or a manufacturing company in Gujarat, the right ERP system can transform your operations and pave the way for a prosperous future.

[outsourcebigdata]

Best data extraction services in USA

In today's fiercely competitive business landscape, the strategic selection of a web data extraction services provider becomes crucial. Outsource Bigdata stands out by offering access to high-quality data through a meticulously crafted automated, AI-augmented process designed to extract valuable insights from websites. Our team ensures data precision and reliability, facilitating decision-making processes.

For more details, visit: https://outsourcebigdata.com/data-automation/web-scraping-services/web-data-extraction-services/.

About AIMLEAP

Outsource Bigdata is a division of Aimleap. AIMLEAP is an ISO 9001:2015 and ISO/IEC 27001:2013 certified global technology consulting and service provider offering AI-augmented Data Solutions, Data Engineering, Automation, IT Services, and Digital Marketing Services. AIMLEAP has been recognized as a ‘Great Place to Work®’.

With a special focus on AI and automation, we built quite a few AI & ML solutions, AI-driven web scraping solutions, AI-data Labeling, AI-Data-Hub, and Self-serving BI solutions. We started in 2012 and successfully delivered IT & digital transformation projects, automation-driven data solutions, on-demand data, and digital marketing for more than 750 fast-growing companies in the USA, Europe, New Zealand, Australia, Canada; and more. 

-An ISO 9001:2015 and ISO/IEC 27001:2013 certified  -Served 750+ customers  -11+ Years of industry experience  -98% client retention  -Great Place to Work® certified  -Global delivery centers in the USA, Canada, India & Australia 

Our Data Solutions

APISCRAPY: AI driven web scraping & workflow automation platform APISCRAPY is an AI driven web scraping and automation platform that converts any web data into ready-to-use data. The platform is capable to extract data from websites, process data, automate workflows, classify data and integrate ready to consume data into database or deliver data in any desired format. 

AI-Labeler: AI augmented annotation & labeling solution AI-Labeler is an AI augmented data annotation platform that combines the power of artificial intelligence with in-person involvement to label, annotate and classify data, and allowing faster development of robust and accurate models.

AI-Data-Hub: On-demand data for building AI products & services On-demand AI data hub for curated data, pre-annotated data, pre-classified data, and allowing enterprises to obtain easily and efficiently, and exploit high-quality data for training and developing AI models.

PRICESCRAPY: AI enabled real-time pricing solution An AI and automation driven price solution that provides real time price monitoring, pricing analytics, and dynamic pricing for companies across the world. 

APIKART: AI driven data API solution hub  APIKART is a data API hub that allows businesses and developers to access and integrate large volume of data from various sources through APIs. It is a data solution hub for accessing data through APIs, allowing companies to leverage data, and integrate APIs into their systems and applications. 

Locations: USA: 1-30235 14656  Canada: +1 4378 370 063  India: +91 810 527 1615  Australia: +61 402 576 615 Email: Sales@aimleap.com

[shitposting-hobbits-to-gallifrey]

Because absolutely no one here seems to understand what this post is actually about, here is what staff is saying here, decoded:

They already *do* have features in place to prevent random web crawlers looking for Machine Learning training data from crawling Tumblr.

To elaborate on how this works: there's a little type of HTML tag you can add to a webpage to tell various types of web crawlers (which aren't just for AI, search engines also use crawlers to find web pages) not to look at the page, although its up to the people who made the crawler to make it respect this setting. (This is why you Tumblr using the phrasing "discourage blahblahblah" around this a lot)

So again, they are already preventing random crawlers on the web from taking ML training data from Tumblr, the new change is (as discussed in other posts) Tumblr is partnering with some ML companies and will, by default, share data from public blogs for ML training purposes with those companies.

However, you can disable this by going to your blog settings on web and turning off the "Prevent Third Party sharing" setting

Also, if you have the already existing setting that blocks the search engine crawlers I discussed earlier (and thus prevents your blog from showing up in search results) turned on, this new setting is also automatically turned on

Hi, Tumblr. It’s Tumblr. We’re working on some things that we want to share with you. 

AI companies are acquiring content across the internet for a variety of purposes in all sorts of ways. There are currently very few regulations giving individuals control over how their content is used by AI platforms. Proposed regulations around the world, like the European Union’s AI Act, would give individuals more control over whether and how their content is utilized by this emerging technology. We support this right regardless of geographic location, so we’re releasing a toggle to opt out of sharing content from your public blogs with third parties, including AI platforms that use this content for model training. We’re also working with partners to ensure you have as much control as possible regarding what content is used.

Here are the important details:

We already discourage AI crawlers from gathering content from Tumblr and will continue to do so, save for those with which we partner. 

We want to represent all of you on Tumblr and ensure that protections are in place for how your content is used. We are committed to making sure our partners respect those decisions.

To opt out of sharing your public blogs’ content with third parties, visit each of your public blogs’ blog settings via the web interface and toggle on the “Prevent third-party sharing” option. 

For instructions on how to opt out using the latest version of the app, please visit this Help Center doc. 

Please note: If you’ve already chosen to discourage search crawling of your blog in your settings, we’ve automatically enabled the “Prevent third-party sharing” option.

If you have concerns, please read through the Help Center doc linked above and contact us via Support if you still have questions.

[corporatetrainingblog]

Why You Should Upskill with Emerging Technology Courses in 2025

In today’s fast-changing tech landscape, staying ahead means continuously upgrading your skills. Businesses that invest in Emerging Technology Courses gain a competitive edge by empowering their teams with the latest tools and knowledge. 

Whether you're a tech team, operations head, or business strategist, ETG’s hands-on training programs can equip you with the skills needed to navigate and thrive in 2025 and beyond.

What Makes Pragati Software’s ETG Unique?

1. Instructor-Led & Flexible Training Formats ETG offers flexible training options—online, offline, or hybrid—to fit your company’s schedule. Courses are instructor-led and highly interactive, including real-world case studies, live projects, and Q&A sessions to ensure practical understanding.

2. Trusted Industry Experts All courses are led by seasoned professionals with over two decades of experience in their respective fields. After completing the program, participants receive industry-recognized certificates.

3. Custom Learning Paths for Every Role These training programs are designed for a wide range of professionals, including:

Business innovation leaders

Technical teams and software developers

Project managers and operations teams

Key Emerging Technology Courses to Explore

Each course is delivered over 3 full days or 6 half-day sessions, making it easy to integrate into your work schedule. Some of the most in-demand ETG courses include:

Machine Learning – Learn how to build smart systems that adapt and improve.

Data Science – Master data analysis to unlock key business insights.

Blockchain – Understand the fundamentals of secure, decentralized systems.

Internet of Things (IoT) – Explore how connected devices are transforming industries.

Web3 – Get ahead of the curve with decentralized internet technologies.

Hyperautomation – Automate business processes using AI, ML, and RPA.

Cloud Computing – Learn scalable, secure, and flexible cloud solutions.

Cyber Security – Strengthen your digital infrastructure against evolving threats.

Data Fabric – Build a connected, consistent data environment across platforms.

Why Enroll in Emerging Technology Training?

✔ Stay Relevant: Keep up with rapid tech advancements. ✔ Drive Innovation: Train your team to think creatively and solve real problems. ✔ Boost Efficiency: Leverage new tech to streamline operations and reduce costs. ✔ Enhance Customer Experience: Use modern tools to better understand and serve customers.

[zilog6502]

Something I heard recently and I think helps a lot is this:

Back when spreadsheeting software was getting really popular, people were saying it was the end of accountancy. This obviously didn’t happen - it just became another tool in the box. AI will do the same once the current craze has died down and people have worked out what it’s actually useful for rather than using it for everything they can think of.

Obviously AI is slightly different because of the unethical webscraping practices of most of the companies who’ve made these really large and popular models, but that doesn’t make AI bad as a whole.

I also feel the need to mention the difference between AI and ML:

AI (artificial intelligence) is just a system that appears to have intelligence - a pocket calculator is an AI because it appears to be very good at mental maths

ML (machine learning) is where a system is trained on vast amounts of data and then uses that to predict the outcome of problems outside of its training data (e.g. all ChatGPT and other Large Language Models (LLMs) do is to predict the most likely ‘token’ (most commonly part of a word) to follow the last n tokens). ML is what all of the hubbub is about and is a subset of AI

This distinction is becoming increasingly academic but I think it’s worth bearing in mind.

Some of y'all will see the word "AI" and freak out without actually processing anything that's being said like a conservative reading the word "pronouns"

[callofdutymobileindia]

Why Take a Machine Learning Course in Chennai? Top Reasons to Enroll Now

As artificial intelligence continues to dominate industries and redefine the future of work, Machine Learning (ML) has become one of the most in-demand skills globally. For aspiring data scientists, engineers, and tech enthusiasts in India, there’s no better place to upskill than Chennai, a city rapidly evolving into a major hub for AI and tech innovation.

Whether you’re a fresh graduate or a working professional, a Machine Learning course in Chennai could be your gateway to a rewarding, future-proof career. In this blog, we explore why Chennai is becoming a popular destination for ML training and the top reasons you should consider enrolling in a course today.

1. Chennai: The Emerging AI & ML Talent Hub

Traditionally known for its automobile and manufacturing industries, Chennai has now transformed into a tech powerhouse, with major IT parks, R&D centers, and startups driving digital innovation. Global tech giants like TCS, Cognizant, Infosys, Wipro, Amazon, and Accenture have established strong operations in Chennai, creating abundant demand for skilled professionals in AI and ML.

According to NASSCOM, Chennai is one of India’s top 5 cities for data science and machine learning talent. A growing number of training centers and institutes offer machine learning courses tailored for both students and working professionals, making it a strategic choice for career growth.

2. Hands-On, Industry-Aligned Curriculum

Enrolling in a Machine Learning course in Chennai means gaining access to industry-aligned, hands-on curriculum. Most reputable institutes design their courses in collaboration with top tech companies, ensuring learners acquire real-world skills that are directly applicable in job settings.

Topics typically covered include:

Python for Machine Learning

Data Preprocessing & Feature Engineering

Supervised and Unsupervised Algorithms

Model Evaluation & Tuning

Deep Learning Basics

Natural Language Processing (NLP)

Computer Vision & Time Series Analysis

Deployment using Flask/Streamlit and cloud platforms

Students also work on live capstone projects, often based on real business challenges, which help them build strong portfolios and gain confidence for job interviews.

3. Expert Trainers and Mentorship

One of the standout benefits of taking a Machine Learning course in Chennai is the opportunity to learn directly from industry experts, PhDs, and AI practitioners. Many institutes hire trainers who have worked with leading global companies and bring valuable insights from the field.

Instructors often supplement lectures with:

Real-life case studies

One-on-one mentorship sessions

Mock interviews and resume reviews

Peer-to-peer discussions and coding challenges

Such hands-on guidance helps learners internalize complex ML concepts and develop critical problem-solving skills.

4. Networking Opportunities in a Thriving Tech Ecosystem

Chennai’s vibrant tech and startup ecosystem provides unparalleled opportunities to network with data scientists, ML engineers, researchers, and entrepreneurs. As you progress through your course, you can attend:

AI/ML meetups

Tech conferences (e.g., DataHack, PyCon, Deep Learning Indaba)

Hackathons and coding bootcamps

Guest lectures from industry leaders

This ecosystem not only boosts your knowledge but also increases your visibility among potential recruiters, collaborators, and mentors.

5. Job Placement Support and Internship Opportunities

Top training institutes in Chennai offer robust placement support, including:

Resume building workshops

Mock technical interviews

Job referrals through hiring partners

Internship opportunities with startups and AI-focused companies

Some institutes even have dedicated career support cells to help students transition smoothly from learning to employment. This is particularly helpful for freshers and career switchers.

Career Roles After Completing the Course:

Machine Learning Engineer

Data Scientist

AI Engineer

Data Analyst

Research Associate – AI/ML

NLP Engineer

Computer Vision Developer

The average salary for ML professionals in Chennai ranges from ₹6 LPA to ₹18 LPA, depending on experience and specialization.

6. Affordable and Flexible Learning Options

Unlike expensive overseas programs, a Machine Learning course in Chennai is highly cost-effective and offers great ROI. Institutes offer a range of pricing models:

Weekend and weekday batches

Full-time bootcamps or part-time courses

EMI options for payment

Hybrid learning (classroom + online)

This flexibility allows working professionals to upskill without quitting their jobs and enables students to learn at their own pace.

7. Learn from the Best: Boston Institute of Analytics (BIA) – Chennai

If you're searching for a trusted Machine Learning course in Chennai, the Boston Institute of Analytics (BIA) is a leading name in the field.

What sets BIA Chennai apart?

Globally recognized certification

Industry-designed curriculum

Real-world projects and case studies

Hands-on training from experienced faculty

100% placement assistance

Alumni working at top firms across India and abroad

BIA’s classroom courses provide a perfect balance of theory, practical skills, and industry exposure—making it an ideal choice for anyone serious about building a successful ML career.

8. High Demand for Machine Learning Professionals in Chennai

From healthcare and banking to e-commerce and manufacturing, companies across sectors in Chennai are adopting AI and machine learning to improve decision-making, automation, and efficiency.

Some key industry applications of ML in Chennai include:

Predictive maintenance in manufacturing

Fraud detection in banking

Personalized recommendations in retail and e-commerce

AI chatbots for customer support

Risk analysis and loan approval in finance

Disease prediction and medical imaging in healthcare

The demand for ML talent is only growing, and enrolling in a course now ensures you’re ready to capitalize on future opportunities.

Final Thoughts

Chennai is rapidly becoming a hotspot for AI innovation, and enrolling in a Machine Learning course in Chennai is a smart, strategic move if you want to enter one of the most future-proof careers today.

With access to top-tier training, expert mentorship, live projects, placement support, and a growing demand for AI talent, Chennai offers the perfect ecosystem to kickstart your journey into machine learning.

So why wait? Take the next step in your career and join a machine learning course in Chennai today. Your future in AI and data science begins here.

[esoanem]

Yes and no

This is something computers are actually really useful for, it's just that you don't need generative AI for it, and more traditional machine learning is better

My previous job was working for a company that did exactly this for the corporate law. Basically, a large part of a corporate lawyer's job is sifting through huge piles of documents almost all of which ends up not being relevant. The idea is that the product we had would do a lot of that work for you, meaning your lawyers can get on with the actual lawyer-ing instead

And the product was generally pretty good! It would identify the type of document, different types of clause within the document, amounts of currency referenced as well as what they related to etc

And it was doing this all before the pandemic and before the initial release of ChatGPT

It was just using traditional machine learning techniques, and letting customers add/remove/edit tags which would go on to refine the model (used by that customer, the data didn't transfer, and so one customer's model would generally be slightly different than another's)

Generative AI, including LLMs like ChatGPT doesn't give you any advantage over traditional ML, and just hides everything away behind a black box much more making it less reliable and harder to correct

The only disadvantage traditional ML has is that the model needs to be trained to your particular situation before it gets really good, whereas LLMs promise to be good right off the bat. But as we've seen, the emperor has no clothes, and it's just that the LLM isn't going to adapt to your data and become better, and it's even likely to start off worse

Artificial intelligence is worse than humans in every way at summarising documents and might actually create additional work for people, a government trial of the technology has found. Amazon conducted the test earlier this year for Australia’s corporate regulator the Securities and Investments Commission (ASIC) using submissions made to an inquiry. The outcome of the trial was revealed in an answer to a questions on notice at the Senate select committee on adopting artificial intelligence. The test involved testing generative AI models before selecting one to ingest five submissions from a parliamentary inquiry into audit and consultancy firms. The most promising model, Meta’s open source model Llama2-70B, was prompted to summarise the submissions with a focus on ASIC mentions, recommendations, references to more regulation, and to include the page references and context. Ten ASIC staff, of varying levels of seniority, were also given the same task with similar prompts. Then, a group of reviewers blindly assessed the summaries produced by both humans and AI for coherency, length, ASIC references, regulation references and for identifying recommendations. They were unaware that this exercise involved AI at all. These reviewers overwhelmingly found that the human summaries beat out their AI competitors on every criteria and on every submission, scoring an 81% on an internal rubric compared with the machine’s 47%.  Human summaries ran up the score by significantly outperforming on identifying references to ASIC documents in the long document, a type of task that the report notes is a “notoriously hard task” for this type of AI. But humans still beat the technology across the board. Reviewers told the report’s authors that AI summaries often missed emphasis, nuance and context; included incorrect information or missed relevant information; and sometimes focused on auxiliary points or introduced irrelevant information. Three of the five reviewers said they guessed that they were reviewing AI content. The reviewers’ overall feedback was that they felt AI summaries may be counterproductive and create further work because of the need to fact-check and refer to original submissions which communicated the message better and more concisely. 

3 September 2024