Or Lenchner, CEO of Bright Data – Interview Series

Or Lenchner, CEO of Bright Data, has led the leading web data collection platform since 2018, prompting expansion, innovation and growth to more than $ 100 million of annual revenue. Bright Data Corporses Fortune 500, leading companies, famous universities, and public sector entities allows access to public web data in actual time and widely. Lenchner is a powerful defender to maintain open and accessible general web data, while emphasizing its decisive role in driving innovation.

What inspired your journey to the data world and AI, and since you became the CEO in 2018, how did the Brett task and see?

You have always been fascinated by the power of data, especially with how it pays decisions and creates fuel. When using it properly, data can also push transparency in business. He gave me the CEO of Bright Data in 2018 became an opportunity to help form how researchers and companies of artificial intelligence provide sources and use public web data.

What are the main challenges facing artificial intelligence teams in obtaining widespread public web data, and how do bright data processes them?

Expansion is still one of the biggest challenges for artificial intelligence teams. Since artificial intelligence models require huge amounts of data, effective assembly is not a small task. Since artificial intelligence models are as good as the data that is trained on it, ensuring access to new high -quality data is a continuous challenge. This is especially true because the web develops in the actual time.

Another great concern is compliance. Data privacy laws and requirements are constantly evolving, so artificial intelligence teams should always be aware of these changes. They also have to understand how to deal with web sites that impose anti -bot’s mechanisms, which can complicate the data collection process.

The platform we built in Bright Data takes care of these challenges. We offer development automated data collection that provides organized data in actual time. Our tools driven by AI-cleaning data and checking health to ensure accuracy. We have strict measures to ensure the collection of legal and moral data for compliance. The idea is to enable artificial intelligence teams to focus on building great models, while we deal with the complexities of data sources.

How high -quality web data contribute to the performance of the artificial intelligence model, and what is the best practices to ensure the accuracy of the data?

High -quality data means complete data and free from biases, and most importantly, accurate. If the data is not present or mired in contradictions and errors, the resulting artificial intelligence model will not lead to expectations.

To achieve accuracy, it is better to source of data from a variety of general sources that have proven reliability. Using only a few, or worse, one data source, leads to problems such as incomplete. Providing multiple sources provides the ability to reference and create a more balanced and well -acting data set. In addition, institutions must consider verifying data and automatic disinfection, to get rid of wrong and inconcantly consistent data.

In bright data, we take all these factors in mind. We offer artificial intelligence teams with actual organized data that have been valid for accuracy. In this way, they can train models with confidence.

What are the biggest moral concerns about collecting public web data today?

Privacy remains one of the largest concerns about collecting public web data. People worry about exposing their data to abuse and abuse. To ensure that the data remains special, it is necessary to emphasize transparency. Institutions that accumulate data should be introduced with regard to the data they collect. It is important to assure the public that their data is used under strict moral guidelines.

Another main anxiety is a monopoly. Some large companies control an enormous amount of data, which creates an unequal field, as only a few have access to the information needed to train artificial intelligence models and pay innovation. This is not how things should be. Public web data should remain accessible to companies, researchers and developers. In this way, the development of artificial intelligence is not concentrated in the hands of a few main players.

Ethics is not a later idea in bright data. They are an integral part of every decision we make. We only follow industry standards – we have set them. In the data collection industry, we lead to determining the correct ethical standards. We want to ensure access to public web data with responsibility and transparency, and in full compliance with global regulations.

How does bright data guarantee compliance with international data privacy systems while continuing to enable data collection on a large scale?

Our institution is committed to adhering to global legal and organizational requirements to collect and use data. We see that we adhere to the requirements of GDP, CPRA, CCPA, and other relevant regulations. More importantly, we carefully follow the customer knowledge protocols (KYC) to ensure only legitimate users to access our basic system. Our data solutions may only be accessed by legal companies and researchers.

Our accepted use policy is also clear in determining data that can be collected and unanimously. This includes responsible use. We have a dedicated compliance team responsible for continuous monitoring of regulations to ensure that we are always aware of the latest legal and regulatory requirements.

Regardless, we still believe that public web data should remain available. Our goal is to provide artificial intelligence teams with the data they need while ensuring compliance with privacy and legal standards.

How to balance business growth while maintaining ethical data collection practices?

We are always thinking about ethics and growth that it is not mutually exclusive. The confidence of our customers and the relationship we build with them are concerns of utmost importance. We understand that we may achieve long -term success only if we collect data under transparent conditions and according to the laws in force.

Thus, we put a strict examination protocol for our users. This is designed to ensure the use of the data we collect morally. We allocate time, effort and resources for compliance and security to protect our customers and the public in general. By monitoring ethical data collection, we succeed in commercially with contributing to the creation of an ecosystem of transparent and responsible artificial intelligence.

How does the bright data remain at the forefront of organizational changes in the privacy of data?

We understand that our data use must inevitably change to reflect the changes in relevant laws and regulations. As such, we regularly consult legal experts and communicate with organizational bodies. We also engage in discussions with legislators and other policy -building participants, and provide inputs in formulating meaningful data regulations. We aim to achieve a balance between innovation and data privacy.

The data collection framework and our use develop when issuing new laws and reviewing regulations. Our compliance team that updates our data use policies proactively to ensure that our basic system always complies with completely. Moreover, we manage customer education initiatives to enhance the use of ethical data.

What are the trends arising in collecting artificial intelligence data that companies should be familiar with?

Real -time data collection has become necessary for artificial intelligence models today. It is important for them to access the latest or latest data to provide a high level of accuracy and provide better user experiences.

Another noticeable direction is to rely on the artificial data used to increase data, as AI creates data that complements data collections collected from the real world scenarios.

I also see a strong interest in pursuing explanation artificial intelligence. Most artificial intelligence models nowadays suffer from the influence of the black box, or the lack of transparency in decision -making processes. Companies seek to change this model by creating Amnesty International models that can detail how they reach the outputs or decisions they make.

Finally, companies realize the increasing data privacy. For this reason, artificial intelligence techniques aimed at maintaining data privacy, such as uniform learning, have become in demand. Institutions want to increase the training of the artificial intelligence model without any concessions on the privacy of user data.

We make sure that we are at the top of these trends, so that we can create solutions that allow artificial intelligence teams to maintain a competitive advantage.

How do you see the agents working in artificial intelligence and automation to change the data collection scene?

Currently, artificial intelligence models use organized data collections that are often collected. These data groups also pass through pre -processing, disinfection and other procedures that usually involve human intervention. This is scheduled to change in the near future with the appearance of artificial intelligence agents to collect independent data and data processing to train artificial intelligence. It makes it possible to learn automatically from web data in an unprecedented scale.

We have created an infrastructure that supports the deployment and development of artificial intelligence agents, allowing smooth access to high -quality data on the web. This technology provides advanced artificial intelligence systems to interact continuously with dynamic web data, learning from them, growth is greater and better.

The artificial intelligence agent can transfer industries because they allow artificial intelligence systems to access and learn from constantly changing data groups on the web rather than relying on fixed and manually processing data. This can lead to Banking or Cybersecurity Ai Chatbots, for example, able to make decisions that reflect the recent facts. This leads to great progress in efficiency and more areas of automation.

In bright data, we only can transform this shift in the data collection scene. We believe that we are at the forefront, and we offer a technique that consumes the next generation of artificial intelligence. We are excited to help companies and artificial intelligence teams because they harness the full potential of artificial intelligence agents of their operations.

Thank you for the wonderful interview, readers who want to know more should visit the bright data.