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Here is information about how ChatGPT and AI with Python have been combined in data science

Today, were diving headfirst into the worlds of Python, Python-based artificial intelligence, and Python-based machine learning. Integration of ChatGPT and AI with Python in Data Science giving great results.

The need for powerful tools to analyze and interpret data has grown in importance as data continues to increase in value in todays business environment.

Thats where ChatGPT and AI and machine learning come in. They help us make sense of complicated data sets and find hidden insights.

However, manually analyzing the vast amount of data available can be an intimidating and time-consuming endeavor.

That is where computerization and chatbots like ChatGPT come in.

In the field of data science, these potent instruments are valuable assets because they can quickly and effectively analyze, process, and generate insights from large volumes of data. It now resembles making things using AI.

ChatGPT in Data Science Clarifications:

Numerous Data science applications can benefit from ChatGPTs potent capabilities. Lets take a look at a few of the ways ChatGPT can be incorporated into the Data Science workflow:

1. Business Understanding: Data science teams can use ChatGPT to better communicate with stakeholders and gain a deeper comprehension of the issue and the potential application of predictive models. In the not-too-distant future, chatbots might interact with stakeholders to investigate project requirements, such as the potential applications of the model and the modifications to organizational procedures required to make use of the model.

2. Web Scraping: Data can be scraped from websites and other online sources with ChatGPT. This can be particularly valuable for information researchers who need to assemble a lot of information rapidly and proficiently. Data scientists can save time and focus on analyzing the data rather than collecting it by automating the web scraping process with ChatGPT.

3. Exploration and Analysis of the Data: Additionally, data exploration and analysis are possible with ChatGPT. ChatGPT can assist data scientists in quickly identifying trends and patterns in data sets by utilizing natural language processing. This can be particularly helpful for huge informational collections that would require hours or even days to physically break down.

4. Modeling: Current adaptations of ChatGPT can assist with creating AI code (e.g., in Python or R). As a result, utilizing ChatGPT in a data science project is as easy as speeding up the development of R and Python code to clean and store data, create visualizations, and build ML models (perhaps by pairing a human with a chatbot). Keep in mind that there are already applications that use ChatGPT as an assistant within an editor.

5. Visualization of Data: Data visualization is another possibility with ChatGPT. By producing human-like reactions in light of the information, ChatGPT can make intelligent representations that permit clients to investigate the information in previously unheard-of ways. Using conventional data visualization techniques, data scientists may miss important insights if they dont use this.

6. Machine Learning: Machine learning applications can make use of ChatGPT. Machine learning models can benefit from ChatGPTs ability to learn from and improve their predictions. In applications like predictive analytics, where precise predictions are crucial, this can be especially useful.

By and large, ChatGPT is a useful asset that can be coordinated into various information science applications. Data scientists can save time and focus on data analysis by automating tasks like web scraping and data exploration with ChatGPT. ChatGPT can also assist users in exploring and comprehending data in novel and exciting ways by generating human-like responses based on the data.

7. Deployment: Depending on the organization and the context of the data science project, deployment requirements vary greatly. A companys processes may need to change as a result of deployment, which may be necessary for the company to use machine learning insights effectively. In this present circumstance, a chatbot could assist individuals with understanding how their job is developing and how to best use ML bits of knowledge. Deployment of an ML system may also require IT infrastructure and support. In this present circumstance, a bot could help discharge designers arrange and convey a strong foundation for the new ML arrangement.

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ChatGPT and AI have been Combined in Data Science with Python - Analytics Insight

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Data Science Workshops and Industry Panel | Calgary Economic - Calgary Economic Development

Rabia Gondur, FCLC 22, and current GSAS student presents her research at the Data Science Symposium. Photos by Marisol DiazFordham faculty and students demonstrated how theyre using data to enhance medical research, examine the impact of social media, prevent AI attackers, and more at the Doing Good with Data symposium, held at the Law School on April 11.

Its particularly exciting to see how data science is being used to enhance ethically informed and motivated research, said Ann Gaylin, dean of the Graduate School of Arts and Sciences. Im also pleased to note how this research aligns so closely with GSASs mission of graduate education for the global good.

Xiangyu Tao, a fourth-year doctoral student in the applied developmental psychology program, used survey data to illustrate social medias effects on LGBTQ+ students. She found that the more time the students spent on social media, the more discrimination and hateful language they were exposed to, which caused higher levels of stress, anxiety, and depression.

Taos research also found that while LGBTQ+ students reported some positives regarding social media, such as finding a community and resources online, they did not outweigh the negatives. She shared her findings with members of the undergraduate Queer Student Advisory Board who had some insights.

[A] member brought up that positives that happen on social media fade away when you close your phone, but the negatives on social media, like discrimination, will linger and impact a persons mental health, she said.

Xiangyu Tao, a fourth-year doctoral student in the applied developmental psychology program, explains her research into social medias impacts.

Understanding the relationship between brain activity and behaviors is a main focus of neuroscience, said Rabia Gondur, an integrative neuroscience major who graduated from Fordham College at Lincoln Center in 2022 and is currently part of the accelerated masters program in data science in the Graduate School of Arts and Sciences.

How do we relate these rich, complex naturalistic behaviors to their simultaneously recorded neural activity? With our research we are trying to answer this question, she said.

But Gondur noted that oftentimes models for documenting these, are restricted to only one data modality, so either neural activity or behavior, but usually not in conjunction.

With Stephen Keeley, an assistant professor of natural sciences, Gondur worked to combine existing models to better show how that conjunction of neural activity and behavior is related. She gave an example of a fly and showed how the model tracked both the neural activity in the brain taking place and what the behavior of the fly was, such as moving its left limb or right limb.

We hope that [this combined]model can be a general tool for understanding the relationship between the brain and behavior, she said.

Nolan Chiles, a senior at Fordham College at Rose Hill majoring in integrative neuroscience, explains his research into how algorithms could support future drug discovery efforts.

Nolan Chiles, a senior at Fordham College at Rose Hill majoring in integrative neuroscience, worked with chemistry professor Joshua Schrier to conduct research on a classification algorithm that he hopes, with some additional work, can be used for drug discovery.

The predominant way that we discover new drugs, say for HIV, [is by trying]to find molecules that are effective in inhibiting infection, he said.

Traditionally this is done through a method called High Throughput Screening, which involves testing many molecules, often blindly, Chiles said, for how effective they are.

This is often costly and time inefficient, and so we are beginning to find other ways of using computational prescreening so that we can cut down on the number of molecules that we actually have to evaluate in the lab, he said.

Courtney King, a doctoral student in computer science who received her masters degree in the subject from the Graduate School of Arts and Science in 2022, worked with Juntao Chen, an assistant professor of computer and information sciences, to examine how an attacker can manipulate data to make something like a chatbot do something it was not made to do.

King gave the example of the chatbot Tay from Microsoft, which was not supposed to be able to be taught offensive language, but through policy poisoning, Twitter users were able to make her say racist things.

Data poisoning is reported as a leading concern for industry applications, King said.

Their research helped to identify a potential vulnerability where an attacker can trick the machine learner into implementing a targeted malicious policy by manipulating the batch data, such as a chatbot saying racist phrases. By pointing out this vulnerability, the researchers showed that it is crucial for a system to actively protect its stored data, and specifically its sensor data, for trustworthy batch learning. Kings paper stated that future work could include exploring how to detect or protect against this type of attack.

Courtney King, a doctoral student in computer science, describes her research into policy poisoning.

Other presentations included a look into Project FRESH Air and how the citizen science program uses monitors to detect air quality at schools in the Bronx and Manhattan; how functional difficulties, such as vision impairment, can be mapped by region; and how algorithms can be used to identify data vulnerable to ransomware attacks.

Gaylin praised all of the presenters, particularly the graduate students, for their research.

Its heartening to see that graduate students in the first cohorts of our two newest programsthe Ph.D. in computer science, and the dual masters degree in economics and data sciencehave hit the ground running, she said. These students are our future.

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'Doing Good with Data': Faculty and Students Present Research - Fordham News

Comet, a leading platform for managing, visualizing and optimizing models from training runs to production monitoring, announced a new suite of tools designed to revolutionize the workflow surrounding Large Language Models (LLMs). These tools mark the beginning of a new market category, known as LLMOps. With Comets MLOps platform and cutting-edge LLMOps tools, organizations can effectively manage their LLMs and enhance their performance in a fraction of the time.

Comets new suite of tools debuts as data scientists working on NLP are no longer training their own models; rather, theyre spending days working to generate the right prompts (i.e. prompt engineering or prompt chaining in which data scientists create prompts based on the output of a previous prompt to solve more complex problems). However, data scientists havent had tools to sufficiently manage and analyze the performance of these prompts. Comets offering enables them to embrace unparalleled levels of productivity and performance. Its tools address the evolving needs of the ML community to build production-ready LLMs and fill a gap in the market that until now has been neglected.

Previously, data scientists required large amounts of data, significant GPU resources, and months of work to train a model, commented Gideon Mendels, CEO and co-founder of Comet. However, today, they can bring their models to production more rapidly than ever before. But the new LLM workflow necessitates dramatically different tools, and Comets LLMOps capabilities were designed to address this crucial need. With our latest release, we believe that Comet offers a comprehensive solution to the challenges that have arisen with the use of Large Language Models.

Comet LLMOps Tools in Action

Comets LLMOps tools are designed to allow users to leverage the latest advancement in Prompt Management and query models in Comet to iterate quicker, identify performance bottlenecks, and visualize the internal state of the Prompt Chains.

The new suite of tools serves three primary functions:

Integrations with leading Large Language Models and Libraries

Comet also announced integrations with OpenAI and LangChain, adding significant value to users. Comets integration with LangChain allows users to track, visualize, and compare chains so they can iterate faster. The OpenAI integration empowers data scientists to leverage the full potential of OpenAIs GPT-3 and capture usage data and prompt / responses so that users never lose track of their past experiments.

The goal of LangChain is to make it as easy as possible for developers to build language model applications. One of the biggest pain points weve heard is around keeping track of prompts and prompt completions, said Harrison Chase, Creator of LangChain. That is why were so excited about this integration with Comet, a platform for tracking and monitoring your machine learning experiments. With Comet, users can easily log their prompts, LLM outputs, and compare different experiments to make decisions faster. This integration allows LangChain users to streamline their workflow and get the most out of their LLM development.

For more information on the new suite of tools and integrations, please visit Comets website,comet.com/site/products/llmops

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Comet Unveils Suite of Tools and Integrations to Accelerate Large ... - insideBIGDATA

Data (opens in new tab) offers businesses an almost endless list of benefits, from increasing revenue and customer (opens in new tab) retention to improving decision-making and streamlining operations. This makes it an incredibly valuable asset from day to day, but even more so during tough economic times, such as those experienced across the world during the past few years. But many organizations face difficulties in extracting the value from their data - from failing to ensure that analysts, data scientists, developers and engineers work together effectively, to having such relentless demand for business insights that the in-house data team is overwhelmed.

Python (opens in new tab) is an equalizer which can help every part of a data operation to work together. Python is now the most popular language for data science, used by 15.7 million developers globally. It provides an open source framework that enables data teams to deliver cutting-edge data insights rapidly and efficiently. For business leaders, it can be a key differentiator for advanced data analytics.

Python can be seen across many aspects of our lives, however, not everyone may realize it. It is the basis of the Netflix algorithm and the software that controls self-driving cars that you see on the streets. As a general-purpose language, Python is designed to be used in a range of applications (opens in new tab), including data science, software and web development, and automation. Its this versatility along with its beginner-friendliness that makes it accessible to everyone, allowing teams of machine learning (ML) and data engineers, and data scientists to collaborate with ease.

Python has a rich ecosystem of open source libraries that are often targeted for cyber attacks. That is the reason why it is important to proactively address how users access and interact with open source (opens in new tab) tooling in an organization. Python is developed under an open source license, making it freely usable and distributable. For businesses, an open source approach offers distinct advantages. There is a vast community of developers contributing to Python projects, making it easier for organizations to collaborate and achieve their goals. With its rich ecosystem of open source packages, businesses can leverage Python to accelerate projects, without having to deal with the complexity of deploying third-party applications. Its for these reasons that Python has become so popular in the data science field.

Another key aspect of Pythons appeal is speed. In many data analytics use cases, the Python code tends to be simple requiring just a few lines which means that time to market is reduced. This makes Python a natural fit for artificial intelligence (AI) and its algorithmic density. In Python, developers can build logic with as much as 75% less code than other comparable languages.

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Torsten Grabs is Senior Director of Product Management for Snowflake.

According to the latest Python Developers Survey, data analysis is now the single most popular usage for Python, cited by 51% of developers, with ML also among the top uses of the language, cited by 38%. Python provides data scientists with over 70,000 libraries that can be used in any given task. These libraries contain bundles of code, which can be used repeatedly in different programs, making Python programing simpler and more convenient as data scientists will rarely have to start from scratch. Take Streamlit as an example. As a Python-based library, its specifically designed for developers and ML engineers to rapidly build and share ML and data science apps.

For businesses hoping to get to grips with ML for the first time, Python is a clear winner. It offers concise code, allowing developers to write reliable ML solutions faster. This means developers can place all of their efforts into solving an ML problem, rather than focusing on the technical nuances of the language. Its platform-independent, allowing it to run on almost every operating system, which makes it perfect for organizations that dont want to be locked into a proprietary system. As a result, Python improves how cross-functional teams of data scientists, data engineers, and application developers can collaborate in taking ML models from experiments into production - which is one of the key challenges ML practitioners face according to the Anaconda State of Data Science report.

Across industries, Python is making a fundamental difference in how businesses operate, saving time, money, and better utilizing their employees skills. For example, in healthcare, the principal application of Python is based on ML and natural language processing (NLP) algorithms. Such applications include image diagnostics, NLP of medical documents, and the prediction of diseases using human genetics. Patient data is highly confidential, so secure and well-governed processing of such data is essential: this is a key challenge for organizations in the healthcare sector.

The industry widely recognizes the importance of Python, having set up the NHS Python Community. Led by enthusiasts and advocates of practice, the community champions the use of the Python programming language and open code in the NHS and healthcare sector.

Elsewhere, in the utility sector, Python is being adopted to open up new applications to help customers save money and energy. Take EDF as an example - the energy giant moved away from legacy systems in order to have a more unified view of its data. A crucial aspect of this involved utilizing Python to enable data scientists to bring ML models into production. By taking an integrated approach, the company is able to better understand the requirements of its customers and develop new products via ML techniques. As a result, EDF can better support financially vulnerable customers, setting up strategies if they start to face difficulties, and predicting it before it happens.

For most scenarios, whether its analytics, machine learning or app development, Python is not the only language being used. Rather it's often paired with SQL, Java and other languages used by different teams. Integrating Python into data platforms provides organizations with a unique way to create their own applications to derive business value from their data across teams and programming language boundaries. Doing so in a streamlined single cloud service removes much of the expense and complexity traditionally associated with building and managing data-intensive applications catering to different programming language preferences from different teams. Using a cloud (opens in new tab) data platform along with the languages that developers are already comfortable with offers a simpler, faster way to derive business insights from data.

Business leaders need to ensure they are taking advantage of their data while empowering their data scientists, data engineers and developers to collaborate effectively. They also need to be proactive in how open source is used to ensure sensitive data is protected. Python offers data teams the flexibility, performance and speed to turn data into actionable insights, providing an invaluable competitive edge. Going forward, it will be an essential tool for any business looking to operationalize ML insights and grow their business, even in the toughest of times.

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Python: the 'equalizer' for advanced data analytics - TechRadar