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How cloud computing is helping Indian businesses – India TV News

Globally, C-suite and Senior Leadership have prioritized Cloud adoption in the business. A similar pattern has emerged in Indian businesses. According to NASSCOM, Cloud technology will account for roughly 5%-8% of GDP by 2026 and has the potential to create unprecedented growth in employment opportunities in this shared digital infrastructure space aka Cloud.

Indian Government has been supporting Cloud adoption by devising policies and providing support to MSMEs (Ministry of Micro, Small and Medium Enterprises) by creating an appropriate ecosystem. The Digital MSME and the Digital India program has led the transformation for Indian businesses and have encouraged them to use Cloud platforms for communications and information technology applications. These initiatives have helped MSMEs to cut on CAPEX (Capital Expenditure) infrastructure and move to cutting-edge applications and infrastructure economically.

Various cloud providers like AWS, GCP, and Azure are building platforms catering to the need of various businesses and providing support for them to implement cloud in their businesses without having adequate technical know-hows. Creating accounts, connecting with cloud platforms, storing files, or accessing cloud-based SaaS apps have been made very easy for businesses and have helped them come to a place from where they can scale their business and have business continuity and resilience.

Cloud technologies and platforms have provided innumerable opportunities for Indian businesses with access to high-end computing, cutting-edge software, pay-per-use model and have helped businesses move from CAPEX model to OPEX (Operating Expense) model.

The various advantages of Cloud in terms of infinite storage, data security, data encryption, data safety, and flexibility & reliability have helped businesses focus on core business drivers, business agility and go to market strategies. Cloud features like fail fast, create quick proof-of-concept have led to innovation and multifold growth creating new products and customized service offerings for our customers in turn creating enhanced customer satisfaction.

While there are multiple benefits of adopting cloud, there are various challenges as well that our Indian businesses need to take care of prioritization of migration, lack of planning, employee training and unrealistic timelines. One of the common myths among businesses is to have immediate cost savings from their adoption of cloud platforms.

On the contrary, there are costs associated with various challenges outlined above but if implemented strategically, investments will not only have better returns but will help businesses with productivity and scalability.

Cloud providers are providing newsletters, seminars, and conducting various events through which Indian businesses can learn what mature cloud players are doing right and create appropriate internal knowledge within their organization.

Businesses and Educational organizations have a symbiotic relationship the more businesses will drive cloud adoption the more educational institutions will create cloud ready courses and workforces thus driving better employment opportunities and in turn help Indias economy & GDP.

While India looks forward to becoming 3rd largest economy of the world, it is time for our businesses to evangelize cloud adoption in their organization and create future-ready workforces.

Authored by -Sharad Kumar, Regional PMO Head, Hanu An Insight company

(Disclaimer: The opinions expressed in this article are those of the author. They do not reflect the views of India TV)

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How Cloud Computing Is Fueling The AI Hype Cycle And … – Digital First Magazine

Lewis Smithingham is the SVP of Innovation at Media.Monks. A problem-solver who identifies innovative digital opportunities, he helps usher brands and partners into the future with creative and technical expertise and the forefront of digital transformation and virtualization. With a background in film and fine arts and passion for gaming, he levels up Media.Monks work at the intersection of creativity and technology. Most recently, he was named to Business Insiders list of the top 24 agency executives leading advertisers charge into the metaverse and Web3. He sits on the World Economic Forum working group on Value Creation in the Metaverse. He is a sought-after speaker, taking the stage at the NVIDIA GPU Technology Conference, Sports Video Group Summit, Siggraph, CES, NAB, IBC and IAB to name a few.

At a time when the possibilities of technology seem limitless, its hard to imagine we could be on the verge of hitting any sort of ceiling. But the truth is, we are. For a while now, experts have been warning that were approaching the computational limit of deviceswhich means we cant pack more transistors onto a microchip nor make the existing devices smaller than they are.

However, the imminent limits of current hardware are meeting a ferocious hunger for new technological innovations, which are mainly driven by software. This poses the question of how were going to be able to advance in areas like AI and VR when computing performance cant keep upat least not while maintaining the same stability and efficiency.

After years of exploring different tools, services, and technologies to exploit their opportunities, Im convinced that the answer to this conundrum can be found in cloud computing.

The Key Enabler of Technological and Cultural Change

As previously mentioned, the biggest drivers of technological and cultural change are now in software, not devices (although, if you try Half-Life: Alyx in a VR headset, you may beg to differ). Years ago, smartphones wildly evolved from one generation to the next, whereas now they introduce almost no new features. Yet as hardware suffers from the computational plateau, software and GPU are taking off. The improvements in Unreal Engine and Unity, the development of ICVFX, the virtualization of broadcast production and the sudden and rapid onslaught of generative AI are some of the most prominent examples.

At the core of this change lies the fact that people want a type of internet thats experiential, meaningful and useful. ChatGPT reaching one million users in five days is not only a reflection of how good the technology is, but also how much value people are finding in it.

If we want to continue to ride that wave, we have to invest in cloud computing. Considering where Moores Lawcoined by the late Gordon Moore, who recently passed awayis at today (flat and potentially inapplicable), computing has to shift off-device. Unless computers and phones get substantially bigger (thank you, but no) cloud computing will grow as the most valuable infrastructural tool in society.

While digital businesses will still face some profitability challenges, much of this new wave is, quite simply, very useful to the vast majority. And when technology meets cultural needs, commerce waterfalls naturally off their backeven in a recession. People are willing to pay for tools that they find value in, and investors are gazing longingly at the biggest AI startups. Its the reason OpenAI has become profitable, increasing its intake and revenue at a speed that many other technology startups have never seemed able to reach.

The Solution to the Content Distribution Bottleneck

For those of us in the business, its sad to see Moores law coming to an end, even though it will leave a lasting impact. The number of transistors in integrated circuits has become a mainstream metaphor for technological change in general, and interestingly enough, it reached the world of marketing. In fact, Moores law has probably been overshot in the content department.

Soon, well find ourselves with more content than we know what to do with. While we have moved from one-directional, one-stop-shop content (cable TV) to ecosystems encompassing the entire customer journey, supply will still loom over demand, and content distribution will face a bottleneck issue.

Im interested in seeing what we come up with to increase distribution in a way thats cost-efficient and sustainable, and cloud computing will rise as the obvious solution in that regard too.

As Matthew Ball said years ago, The tens of billions that will be spent on cloud gaming over the next decade, too, is based on the belief that such technologies will underpin our online-offline virtual future. Cloud computing will be the key to solving the immediate problems we face in the age of AI enablement, new hardware formats and the distribution of content through new and existing channels.

Ultimately, the internet has developed through punctuated equilibrium. It hasnt been a linear evolution; rather, a series of sudden jumps that pushed the technology forward. AI represents the most recent jump, but weve hit an uncanny valley where its almost but not quite exact. In order to get over that feeling, the technology needs to become more sophisticated, and the cloud will play a key role in enabling itas well as allowing us to effectively navigate whatever advancements and new technologies lie ahead.

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Is Business Intelligence as Same as Cloud Computing? – Analytics Insight

Technology advancements have helped cloud computing gain popularity, but although this is significant, it has mostly been driven by technological innovation rather than commercial needs.

On the other hand, business intelligence is prepared to offer past, present, and future insights about your company. This is accomplished by utilizing a variety of technologies, processes, and practices, ranging from data mining and predictive analytics to analytics and reporting. Business intelligence gives your firm the tools to create a business plan based on factual data by giving a precise picture of the business at a certain point in time.

The ability to store both structured and unstructured data in cloud computing makes it the perfect platform for supplying business intelligence applications with data obtained from a variety of devices at any time and from any location. Activities involving corporate analytics are well complemented by the clouds adaptability and scalability.

By the use of storage, networking, and tools that can sort through large data, the democratization of the cloud is now enabling enterprises that have employed business intelligence using on-premises applications and on a restricted scale to reach a whole new level (coupled with analytics capabilities).

Using the cloud, these firms may let their service provider run and manage the infrastructure and the business intelligence applications as many are unable to staff or understand the complexity needed to manage a BI infrastructure. Compared to an on-premises infrastructure, this might be a more affordable option and a better means to spread information.

For instance, enterprises are testing their business intelligence-based application proofs of concept on the cloud before deploying them on-premises. Costs have been decreased, installation times have been cut, and productivity has risen for those who have deployed these apps in the cloud.

Although the advantages are clear, security is still a concern for many businesses that do not want their data to cross international boundaries and come under the control of other governments. There are still other difficulties, such as the problem of moving vast volumes of data, issues with serviceability, and the complexity brought on by integration.

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How Cloud Computing Solutions Help with Environmental Sustainability – Green Prophet

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List of global environment prizes.

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Securing cloud tech stacks with zero trust will drive growth of confidential computing – VentureBeat

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For enterprises to realize the potential that real-time datasets can deliver, cloud tech stacks need hardening with zero trust. In this, confidential computing is essential to securing data at rest, in transit and in use.

VentureBeat spoke with CIOs from banking, financial services and insurance industries who say they are at various stages of piloting confidential computing to see how well it handles their compliance, regulatory reporting and real-time auditing of data transactions. Notably, compliance and support for zero trust frameworks are emerging as the killer apps.

One CIO who spoke on condition of anonymity said that their board of directors team assigned to risk management wants to see proof that data is secured during use within protected CPU enclaves and Trusted Execution Environments (TEEs), two foundational elements of confidential computing.

Board members on risk management teams recall Meltdown and Spectre vulnerabilities that target processors that rely on branch prediction and advanced speculative actions. CIOs and CISOs say boards need to see pilot data and simulated attacks thwarted before they go into production with confidential computing.

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Based on period pilots that VentureBeat is briefed on, its clear that confidential computing strengthens zero trust in multicloud tech stacks on which highly regulated businesses rely on. Compliance, privacy, and security use cases, particularly on public cloud, have gained the most significant traction, accounting for 30 to 35% of the worldwide market, according to Everest Groups report Confidential Computing: The Next Frontier in Data Security. And, the confidential computing market is predicted to grow to $54 billion by 2026.

Confidential Computing is a cloud computing technology that secures data during processing by isolating sensitive data in a protected CPU enclave. The contents of each enclave, including the data and analysis techniques, are only accessed with authorized programming codes, remaining invisible and protected from external access.

Confidential computing is gaining momentum because it provides greater data confidentiality, data and code integrity than current security technologies protecting cloud tech stacks and infrastructure.

The Confidential Computing Consortium (CCC) is instrumental in promoting and defining confidential computing across the industry. The CCC is a Linux Foundation project that combines the efforts of hardware vendors, cloud providers and software developers to help increase the adoption and standardization of TEE technologies.

TEEs protect proprietary business logic, analytics functions, machine learning (ML) algorithms and applications. Founding members include Alibaba, Arm, Google, Huawei, Intel, Microsoft and Red Hat. The CCC defines confidential computing as protecting data in use by computing in a hardware-based TEE.

Whats working in confidential computings favor with boards is how effective it is at ensuring regulatory compliance. Its also proven to be effective at enforcing end-to-end security and least privileged access to data at rest, in transit and in use. CIOs and CISOs tell VentureBeat that they expect confidential computing to be complimentary to their Zero Trust Network Access (ZTNA) frameworks and supporting initiatives.

John Kindervag created zero trust and currently serves as SVP forcybersecuritystrategy and is a group fellow at ON2IT Cybersecurity. He is also an advisory board member for several organizations, including to the offices of the CEO and president of theCloudSecurityAlliance.

He recently told VentureBeat that the biggest and best-unintended consequence of zero trust was how much it improves the ability to deal with compliance and auditors. And, he said that a Forrester client called and informed him how perfectly aligned zero trust was with their compliance and audit automation process.

Mark Russinovich, CTO and technical fellow of Microsoft Azure writes that: Our vision is to transform the Azure cloud into the Azure confidential cloud, moving from computing in the clear to computing confidentially across the cloud and edge. We want to empower customers to achieve the highest levels of privacy and security for all their workloads.

Cloud platform providers endorsed and began integrating CCCs requirements into their product roadmaps as early as 2019, when the CC was formed. Whats guiding cloud platform providers is the goal of providing their customers with the technical controls necessary to isolate data from cloud platform operators, their operators, or both.

Microsofts Azure confidential computing is considered an industry leader because their DevOps teams designed the platform to go beyond hypervisor isolation between customer tenants to safeguard customer data from Microsoft operator access.

CIOs and CISOs have identified to VentureBeat what theyre looking for when it comes to a baseline level of performance with confidential computing. First, remote attestation needs to be proven in live customer sites with referenceable accounts willing to speak to how they are using it to check the integrity of the environment. Second, trusted launch workflows and processes ideally need to be cloud-based, in production, and proven to validate virtual machines starting up with authorized software and continuous remote attestation to check for customers.

Martin G. Dixon, Intel fellow and VP of Intels security architecture and engineering group writes that, I believe the zero trust concepts shouldnt stop at the network or system. Rather, they can be applied down inside the silicon. We even refer to infrastructure on the chip as a network or network on a chip.'

Part of that vision at Intel included the need for attestation to become more pervasive and portable to fuel confidential computings growth, starting at the silicon level.

To address this, the company introduced Project Amber, whose goals include providing independent attestation, more uniform, portable attestation and improved policy verification.

With the introduction of Project Amber, Intel is taking confidential computing to the next level in our commitment to a zero trust approach to attestation and the verification of compute assets at the network, edge and in the cloud, Greg Lavender, Intels CTO said at the companys Intel Vision conference last year.

He continued that Intel is focused on extending attestation services in the cloud data center in the edge computing environments to provide unprecedented security. The Intel Software as a Service offering Project Amber is a trusted service solution that will provide organizations with independent verification and trustworthiness of customer assets no matter where they run.

Getting silicon-based zero trust security right needs to start with TEEs hardened enough to protect sensitive data at rest, in transit and in use. Migrating zero trust into silicon also strengthens authentication and authorization, taking identity and access management (IAM) and privileged access management to the hardware level, which makes it harder for attackers to bypass or manipulate authentication systems and improves the security of confidential computing environments.

Additional benefits of moving zero trust into silicon include encrypting all data and ensuring a higher level of data integrity and applying zero trust principles to data encryption and authentication. With zero trust frameworks requiring continuous security configuration and posture validation for all users and devices, supporting monitoring in silicon will reduce the overhead on cloud platform performance.

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Cloud computing firm Rackspace to lay off 4% of workforce – ETCIO South East Asia

San Francisco, March 28 (IANS) US-based cloud computing company Rackspace Technology has said it would lay off about 4 per cent of its workforce globally amid tough macroeconomic conditions.

According to San Antonio Express-News, the layoffs will cut nearly 275 employees across the company's global workforce.

However, it is not confirmed how many employees would be let go from the company's San Antonio headquarters or what their roles would be.

According to the recent US Securities and Exchange Commission filing, Rackspace had about 700 local employees in December, and its global employment was about 6,800 in 23 countries, including 3,100 in North America.

"Like so many companies that are feeling the effects of the macroeconomic downturn, Rackspace is no exception," Casey Shilling, chief marketing officer, was quoted as saying.

"During this uncertain time, it is important that we align our cost structure to the demands of the business. This requires some elimination of roles across the company," she added.

The company said that it will provide severance and other resources to those employees who are laid off, the report mentioned.

-IANS

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Why 5G and edge computing are key to retail success on Microsoft Cloud – CIO

The retail industry is transforming rapidly. Modern retailers rely heavily on automation for managing inventory, shelf design, customer service, and logistics. Video cameras and sensors that allow for unique store design help to enhance the customer experience. Technology is truly powering retail transformation, setting modern stores apart from traditional brick-and-mortar ones.

It is no easy feat sending all these video streams and sensor data to the cloud for real-time analysis. High bandwidth is required to move heavy data streams. So is low latency for quick data processing and decision making, especially when robotics is involved.

This is where edge computing and edge-native applications become relevant for retail stores. They allow computing to occur closer to the source of dataright inside the store. Coupled with a private 5G communication network, retailers can deploy cost-effective and high performing edge-native applications.

At the same time, companies must maintain secure environments and prevent fraud. According to a recent Microsoft blog, organizations can use security and compliance solutions in Microsoft 365 E5 to have visibility into their threat landscape and leverage built-in AI and machine learning in Microsoft Sentinel and Microsoft Defender for Cloud to proactively manage threats and reduce alert fatigue.

Read the full blog post to learn more.

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Introduction to Data Science with Python: How is it Beneficial? – Analytics Insight

Learn how beneficial data science with Python in this simplified guide with meaningful resources

The need for more effective and efficient data storage increased significantly as the globe entered the era of big data in recent decades. Businesses utilizing big data put a lot of effort into developing frameworks that can store a lot of data. Eventually, frameworks like Hadoop were developed, aiding in the storage of enormous volumes of data.

When the storage issue was resolved, attention turned to processing the data that had already been saved. Data science has emerged as the method of the future for handling and evaluating data in this situation. Data science is becoming a crucial component of any industry dealing with massive volumes of data. Businesses currently employ experts and data scientists who take the data and transform it into a useful resource.

Lets now get into data science and the advantages of using Python for data science.

Lets start by studying data science and then using Python to learn about it. Finding and examining data in the actual world is fundamental to data science, which then employs this knowledge to address practical business issues.

Now that you are aware of what data science is, lets first discuss Python before delving deeply into the subject of data science with Python.

We require a programming language or tool, such as Python, for data science. Although there are other data science tools, such as SAS and R, this post will concentrate on Python and how it may help with data science.

Python has recently gained a lot of popularity as a programming language. Its usage in data science, the Internet of Things, artificial intelligence, and other technologies have increased its appeal.

Since it has expensive mathematical or statistical features, Python is utilized as a programming language for data research. That is one of the key explanations for why Python is used by data scientists all around the world. Python has emerged as the preferred programming language, particularly for data science, if you follow patterns over the previous few years.

Python is one of the most popular programming languages for data science for several additional reasons, including:

Speed: Python is comparatively quicker than other programming languages in terms of speed.

Availability: There are several packages created by other users that are readily available and may be utilized.

Design objective: Pythons syntactic responsibilities are simple to comprehend and intuitive, making it easier to create applications with intelligible code.

Python is a straightforward programming language to learn, and it supports certain fundamental operations like adding and printing statements. But, you must import certain libraries if you wish to undertake data analysis. Many instances include:

Pandas: Tool for working with structured data.

NumPy: A powerful library that helps you create n-dimensional arrays

SciPy: Offers scientific features like Fourier analysis and linear algebra

Matplotlib: Mostly used for visualization.

Scikit-learn: Used for all machine learning operations.

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The Berkson-Jekel Paradox and its Importance to Data Science – KDnuggets

If you are a Data Scientist or an aspiring one, you will know the importance of statistics in the sector. Statistics help Data Scientists to collect, analyze, and interpret the data by identifying patterns and trends, to then make future predictions.

A statistical paradox is when a statistical result contradicts expectations. It can be very difficult to pinpoint the exact cause, as it is hard to understand the data without the use of further methods. However, they are an important element for Data Scientists as it gives them a lead on what could possibly be causing the misleading results.

Here is a list of statistical paradoxes relevant to data science:

In this article, we will be focusing on the Berkson-Jekel paradox and its relevance to Data Science.

Berkson-Jekel paradox is when two variables are correlated in data, however, when the data is grouped or subsetted, the correlation is not identified. To put it in layman's terms, the correlation is different in different subgroups of the data.

The Berkson-Jekel paradox is named after the first statisticians who described the paradox, Joseph Berkson and John Jekel. The discovery of the Berkson-Jekel paradox is when the two statisticians were studying the correlation between smoking and lung cancer. During their study, they found a correlation between people who had been hospitalized for pneumonia and lung cancer, in comparison to the general population. However, they conducted further research which showed that the correlation was due to smokers being hospitalized for pneumonia more, in comparison to people who did not smoke.

Based on the statistician's first research on the Berkson-Jekel paradox, you may say that more research was required to figure out the exact reasoning behind the correlation. However, there are also other reasons why the Berkson-Jekel paradox occurs.

Statistical reasoning is very important in Data Science, and the main issue is dealing with misleading results. As a data scientist, you want to ensure that you are producing accurate results that can be used in the decision-making process and for future predictions. Making incorrect predictions or misleading results is the last thing on the cards.

There are a few methods that you can use to avoid the Berkson-Jekel Paradox:

If you are dealing with misleading results due to the sample data not being representative of the population, a solution would be to use data from a variety of sources. This will help you to get a more representative sample of the population, research more on the variables, and get a better understanding.

Misleading outputs can hold a company back. Therefore, when working with data, data professionals need to understand the limitations of the data theyre working with, different variables and the relationship between them, and how to reduce misleading results from happening.

If you would like to know more about Simpsons Paradox, have a read of this: Simpsons Paradox and its Implications in Data Science

If you would like to know more about the other statistical paradoxes, have a read of this: 5 Statistical Paradoxes Data Scientists Should KnowNisha Arya is a Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.

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Scientific Journeys: From genetics to the environment and back – Environmental Factor Newsletter

Last October, David Reif, Ph.D., joined what he calls his dream team in the NIEHS Division of Translational Toxicology. As head of the Predictive Toxicology Branch, he leads a multidisciplinary group focused on predicting how individuals and populations respond to environmental exposures. The group aims to improve public health through the development and promotion of cutting-edge, computer-based methods and research models.

Reif recently talked with Environmental Factor about why he transitioned from academia to NIEHS, his journey from genetics to toxicology, and what most excites him about the future.

Environmental Factor: What drew you to NIEHS?

David Reif: Team science. Im a data scientist who enjoys tackling big problems that require expertise beyond my own. I had been a professor for 10 years. I really liked it, but I found that the projects that most motivated me were those involving long-term collaborations. I had been following and using team-built tools coming out of NIEHS, teaching them to my students and incorporating them in my own research. Then this opportunity came up, and I thought it would be great to be on the inside to advance translational toxicological research and predict how gene-by-environment interactions can influence human health.

EF: What makes the Predictive Toxicology Branch unique?

DR: We have a mix that doesnt exist anywhere else in world. Its basically my dream super lab, my dream team. We have computational quantitative biologists, computational chemists, scientists working on geospatial health analytics, and researchers promoting in vitro [cell-based] models and new approach methodologies, all together in one branch.

EF: Can you share how your education and training shaped your career path?

DR: My graduate training was in human genetics and statistics, but I really wanted to study environmental health problems. At that time, in my view, the tools for measuring the environments impact on health were unsophisticated. But scientists had just mapped the human genome, so it was an exciting time, and it felt like everything was possible.

I completed postdoctoral training at the U.S. Environmental Protection Agency, in the just-launched National Center for Computational Toxicology, which had the atmosphere of a startup company. The new center marked the beginning of programs to rapidly test all the chemicals we didnt know about. I was there for seven years as a statistician [principal Investigator], and I really invested in the field. I took formal courses in toxicology, and I started going to Society of Toxicology conferences. I stayed in the toxicology and environmental health research space as a professor at North Carolina State University, even though I joined a genetics department.

EF: Is the shift from genetics to toxicology common?

DR: I don't know if it's common, but I think it's conducive because genetics is a mechanism for both responding to the environment in the near term and a way to transmit information across generations. And you can apply genetics and genomics to lots of different kinds of problems. In some projects here, I'm full circle back to doing clinical studies involving human exposures, but now we have a vastly more sophisticated characterization of the environment to consider.

For example, the exposome, which represents the totality of our environmental exposures, could not be effectively measured back when I was training. Huge progress has been made recently, much like genetic and genomics technologies advanced quickly when I was earning my Ph.D. I feel like we're now in the exposomics era, and it's letting new kinds of science come to the fore.

EF: What is your vision for the Predictive Toxicology Branch?

DR: We have an opportunity to use machine learning, artificial intelligence, and many other cutting-edge tools to produce scientific knowledge that translates directly to human health. It is truly predictive data science that doesn't exist elsewhere because we can make a prediction, build models based on tons of data, and then test those in new experiments. We can predict what we think is going happen, and we can generate the data to validate that prediction.

I want our branch to be a destination for people to come and learn these skills, to establish the branch as a center of excellence for training, and to attract the best scientific talent.

EF: With many chemicals in the environment today, what should the average person know?

DR: One of the best things about government science is that it doesn't have an agenda. Its about truth and robustness. I'm hoping that we can provide those things to people, so when they ask questions about what they are exposed to and how to avoid harm, we can be a trusted source for that information.

I think we're at a cool inflection point where the confluence of technology, talent, and awareness are all coming together. Things have to change, and we're ready to change them.

(Caroline Stetler is Editor-in-Chief of the Environmental Factor, produced monthly by the NIEHS Office of Communications and Public Liaison.)

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