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AI Roles Take Top 2 Spots

LinkedIn Reveals the 25 Fastest-Growing Jobs in the US, with AI Taking Center Stage

Top 3 Fastest-Growing Jobs: Artificial Intelligence Engineer, Artificial Intelligence Consultant, and Artificial Intelligence Researcher

With AI increasingly becoming a part of our daily lives, it’s no surprise that this field has become an in-demand skill in the job market. LinkedIn has recently revealed its list of the 25 fastest-growing jobs in the US, and AI has captured three of the top spots.

Artificial Intelligence Engineer

Ranked number one on the list, Artificial Intelligence Engineer designs, develops, and applies AI models and algorithms to improve business processes and solve complex problems. The skills required for this role include Large Language Models (LLM), Natural Language Processing (NLP), and PyTorch, the open-source library for the Python programming language. Three to four years of prior experience are recommended.

Artificial Intelligence Consultant

Coming in second, Artificial Intelligence Consultant helps organizations adopt and integrate AI technology to meet business goals and improve their operations. The most common skills for this role are LLMs, prompt engineering, and Python programming, with around 4.5 years of prior experience required.

Artificial Intelligence Researcher

Ranked 12th on the list, Artificial Intelligence Researcher advances AI technologies and processes or creates new ones through research, testing, and algorithms. The most common skills needed for this role are deep learning, PyTorch, and LLMs, with at least three years of experience required.

Other AI-Adjacent Jobs: Workforce Development Manager

Another job on the list that’s more AI-adjacent than AI-specific is Workforce Development Manager, ranked fourth. This role involves designing and setting up training programs that help employees learn new skills (including AI) and better align them with the needs of the organization.

Conclusion

LinkedIn’s list highlights the growing importance of AI in the job market, with three AI-related roles taking the top three spots. While AI is often associated with technical roles, many of these jobs require professionals who can bridge the gap between AI and human insight. As Chris Picariello, CEO of Keystone Talent Group, noted, "Companies aren’t just hiring for technical skills anymore, but for people who can leverage AI to enhance human capabilities."

FAQs

Q: What are the top three fastest-growing jobs in the US, according to LinkedIn?
A: Artificial Intelligence Engineer, Artificial Intelligence Consultant, and Artificial Intelligence Researcher.

Q: What skills are required for Artificial Intelligence Engineer?
A: Large Language Models (LLM), Natural Language Processing (NLP), and PyTorch, the open-source library for the Python programming language.

Q: What is the most common industry for Artificial Intelligence Consultant?
A: Technology and internet.

Q: How many years of prior experience are recommended for Artificial Intelligence Researcher?
A: At least three years.

Q: What is the most common industry for Workforce Development Manager?
A: Non-profits.

A.I. in America

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OpenAI’s Vision for Artificial Intelligence in America

OpenAI, a leading artificial intelligence (A.I.) company, has released its economic blueprint for the development of A.I. in the United States. The company’s vision aims to shape how the next presidential administration handles this increasingly important technology.

A.I. in America: A Blueprint for Development

OpenAI’s 15-page document, titled “A.I. in America,” suggests ways that policymakers can spur development of A.I. in the United States, minimize the risks posed by the technology, and maintain a lead over China.

“We believe America needs to act now to maximize A.I.’s possibilities while minimizing its harms,” Chris Lehane, OpenAI’s head of global policy, wrote in the document. “We want to work with policymakers to ensure that A.I.’s benefits are shared responsibly and equitably.”

Spurring Development and Investment

OpenAI is racing to expand the pool of giant computer data centers needed to build and operate its A.I. systems, which will require hundreds of billions of dollars in new investment. The company has called on policymakers to allow significant investment in American A.I. projects by investors in the Middle East, though the Biden administration has been wary of such investment.

“Are those countries going to be building on U.S. rails or are they going to be building on C.C.P. rails?” Mr. Lehane said in an interview, referring to the Chinese Communist Party. He described nations like the Emirates and Saudi Arabia not as allies but more as “swing states” that will choose the United States or China for A.I. investments.

Regulations and Safety

OpenAI has also asked the government to take a light approach when creating regulations meant to ensure the safety and security of technologies built by OpenAI and its American rivals. The company argues that the federal government, not states, should control regulations related to the safety and security of A.I. development.

“That would just create real dissonance, both on a national security and economic competitiveness front,” Mr. Lehane said.

Charm Offensive and Future Plans

Sam Altman, OpenAI’s chief executive, will begin a charm offensive with an event on January 30 in Washington, where he will discuss the future of A.I. development with lawmakers, economists, and Trump administration officials and demonstrate new OpenAI technology that he believes will show the economic power of A.I.

Conclusion

OpenAI’s economic blueprint for A.I. in America aims to shape the future of this technology in the United States. The company’s vision emphasizes the need for policymakers to take a proactive approach to development, investment, and regulation to ensure that A.I. benefits are shared responsibly and equitably.

FAQs

Q: What is OpenAI’s economic blueprint for A.I. in America?
A: OpenAI’s 15-page document outlines ways to spur development of A.I. in the United States, minimize risks, and maintain a lead over China.

Q: What is OpenAI’s stance on investment in American A.I. projects by investors in the Middle East?
A: OpenAI has called on policymakers to allow significant investment in American A.I. projects by investors in the Middle East, arguing that if countries like the Emirates and Saudi Arabia do not invest in U.S. infrastructure, their money will flow to China instead.

Q: What is OpenAI’s position on regulations related to A.I. development?
A: OpenAI has asked the government to take a light approach when creating regulations meant to ensure the safety and security of technologies built by OpenAI and its American rivals, arguing that the federal government, not states, should control regulations related to A.I. development.

Automating SRE Resolutions with AI-Powered Agents

Revolutionizing DevOps with Large Language Models

Over the past two years, one field that has profoundly impacted everyone’s lives is Large Language Models (LLMs). They have seamlessly integrated into our daily lives and continue to evolve rapidly. However, DevOps remains in the exploratory phase when it comes to effectively utilizing the power of LLMs.

Introducing DevOps GPT

Today, I’m thrilled to share a project I’ve been working on for quite some time: DevOps GPT. As we often say, the best way to debug an issue is to dive into the logs. But what if we could take this a step further? With DevOps GPT, we leverage the power of LLMs to analyze logs, provide recommendations, and suggest solutions to complex problems.

What Makes DevOps GPT Different?

1️⃣ Pre-Built SRE Logic: It includes a substantial amount of Site Reliability Engineering (SRE) logic, reducing the need to query the LLM for every error. This makes it faster and more efficient.

2️⃣ Caching Layer: DevOps GPT comes with a built-in caching mechanism, ensuring that similar queries retrieve cached results, significantly improving performance.

Current Features

1️⃣ LLM Support: Supports both OpenAI and LLAMA, with OpenAI set as the default.

2️⃣ Platform Compatibility: Currently, the RPM is compiled for RedHat 🐧, with plans to support additional platforms soon.

3️⃣ Slack Integration: Alerts are integrated with Slack, so you never miss critical updates.

What’s Next?

I plan to integrate DevOps GPT with other LLMs in the future and expand its capabilities to cater to a wider range of use cases.

Get Involved

I’d love for you to give it a try and share your feedback. Your insights will help me improve and make DevOps GPT even better. Let’s shape the future of DevOps together!

Project link: https://github.com/thedevops-gpt/devops-gpt

Conclusion

DevOps GPT is a groundbreaking project that has the potential to revolutionize the way we approach DevOps. By leveraging the power of LLMs, we can analyze logs, provide recommendations, and suggest solutions to complex problems. I invite you to try it out and share your feedback to help shape the future of DevOps.

FAQs

Q: What is DevOps GPT?

A: DevOps GPT is a project that utilizes Large Language Models (LLMs) to analyze logs, provide recommendations, and suggest solutions to complex problems in DevOps.

Q: What makes DevOps GPT different from other LLM-based projects?

A: DevOps GPT includes pre-built SRE logic and a caching layer, making it faster and more efficient than other LLM-based projects.

Q: What platforms is DevOps GPT currently compatible with?

A: DevOps GPT is currently compiled for RedHat 🐧, with plans to support additional platforms soon.

Q: How can I get involved with DevOps GPT?

A: You can try out DevOps GPT and share your feedback to help improve and shape the future of DevOps. You can also contribute to the project on GitHub.

China Open to Elon Musk Acquiring TikTok US

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Chinese Government Discusses Selling TikTok to Elon Musk, Reportedly

Background

Chinese government officials have allegedly considered a scenario where ByteDance sells TikTok’s United States arm to Elon Musk, according to a recent report by Bloomberg. This comes as the Supreme Court is set to rule on a law banning the app on January 19.

Chinese Preference for ByteDance Ownership

While China’s government prefers for TikTok to remain under ByteDance’s ownership, they have considered a sale to Musk as part of a broader plan to work with the incoming Trump administration. In this scenario, Musk’s X would acquire TikTok US, absorbing the platform’s 170 million American users and billions in potential ad revenue.

Rebuttal from TikTok

A TikTok spokesperson has dismissed the report as "pure fiction" in a statement to Variety.

Unclear Knowledge of China’s Plans

It is unclear how much ByteDance and TikTok know about China’s discussions of a Musk sale, potentially highlighting China’s alleged influence over the platform, which led to the law in the first place.

Conclusion

The report raises questions about the future of TikTok in the United States and the level of control China may have over the platform. The fate of the app remains uncertain, with the Supreme Court’s decision on January 19 set to have a significant impact.

Frequently Asked Questions

Q: What is the report saying?
A: The report claims that Chinese government officials are considering selling TikTok’s US arm to Elon Musk, should the Supreme Court uphold the law banning the app on January 19.

Q: What is China’s preference for the ownership of TikTok?
A: China’s government prefers for TikTok to remain under ByteDance’s ownership, but has considered a sale to Musk as part of a broader plan to work with the incoming Trump administration.

Q: How did TikTok respond to the report?
A: A TikTok spokesperson called the report "pure fiction" in a statement to Variety.

Q: What is the significance of the Supreme Court’s decision on January 19?
A: The decision will have a significant impact on the future of TikTok in the United States.

AI Financial Advisers Target Young People Living Paycheck to Paycheck

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AI Financial Advisers: A Closer Look

My goal is to be debt-free by the end of 2025, and as a reporter who often tests new software, I was curious about trying some of the AI financial advisers that have gained popularity in recent years. Hiring a human money manager can easily cost a few thousand dollars, so more people, especially younger users, are turning to AI tools for advice. From Apple’s top charts of free finance apps, I decided to try two well-reviewed options offering up chatbots intended to fix money woes: Cleo AI and Bright.

How AI Financial Advisers Work

Both Cleo AI and Bright encourage users to connect their bank account to the app through a third-party service called Plaid. This allows the chatbots to break down spending habits, help users pay off debt, and build credit. “Using the bank data and what you’ve said to us, Cleo will be your kind of confidant or coach,” says Barney Hussey-Yeo, the company’s CEO and founder. “She’ll provide the right advice and the right products to help you make better financial decisions.”

A Closer Look at Cleo AI

Fair enough, but some of the guidance Cleo gave me veered from that path. While it had engaging moments, like an amicable roast highlighting where I overspent in unnecessary ways, the generative AI tool seemed mainly preoccupied with using my personal data for upselling opportunities. Bright was the same.

Cash Advances and Upselling

For example, I started one conversation pretending to be sad and lacking enough money to buy groceries. According to Hussey-Yeo, Cleo’s core demographic of users are young people who are living paycheck to paycheck and “feel the pain of finances more than most people.” So I thought this would be the kind of thing users shared all the time. The bot feigned sympathy and immediately started encouraging me to check whether I was eligible for a cash advance through the app.

After Cleo cleared my eligibility for a cash advance, I was prompted to sign up for a $6 monthly Cleo Plus membership. The first time I used it, the app offered a $130 cash advance, split into $65 increments over two days. Users technically don’t have to pay a fee for the cash advance if they are willing to wait an estimated three to four business days—a difficult feat for people living between paychecks and a distraction from my goal of paying off previous debts.

Conclusion

In conclusion, while AI financial advisers like Cleo AI and Bright may seem appealing, they may not be the best solution for everyone. Both apps seemed more interested in upselling and offering cash advances than providing genuine financial guidance. As someone trying to pay off debt, I found their advice to be more of a temptation to take on additional debt rather than a real solution to my money issues.

Frequently Asked Questions

Q: What is the purpose of AI financial advisers?

A: AI financial advisers aim to provide personalized financial advice and guidance to users, helping them make better financial decisions and achieve their financial goals.

Q: How do AI financial advisers work?

A: AI financial advisers use machine learning algorithms to analyze users’ financial data, spending habits, and goals. They then provide tailored advice and recommendations to help users achieve their financial objectives.

Q: Are AI financial advisers reliable?

A: While AI financial advisers have the potential to be reliable, they are only as good as the data they are trained on and the algorithms used to analyze that data. It’s essential to research and understand the limitations and biases of AI financial advisers before using them.

Q: Can I trust AI financial advisers with my personal data?

A: AI financial advisers require users to connect their bank accounts and other financial data to provide personalized advice. It’s essential to research the security and privacy policies of the AI financial adviser before sharing your personal data.

Rival OpenAI’s o1-preview in 19 hours

Open-Source Approaches to Artificial Intelligence: A New Frontier

Open-source approaches continue to show promise in democratizing artificial intelligence (AI).

NovaSky’s Sky-T1-32B-Preview

On Friday, the NovaSky research team at UC Berkeley released a new reasoning model, Sky-T1-32B-Preview, that performs comparably to OpenAI’s o1-preview — only it’s open source and was built in just 19 hours for under $450 using eight Nvidia H100 GPUs.

The Best of Both Worlds: Open-Source AI Models

The team developed Sky-T1 by fine-tuning Alibaba’s Qwen2.5-32-Instruct and trained it on data generated with QwQ-32B-Preview, another open-source model comparable to o1-preview. Using synthetic training data can help lower costs.

Data Preparation: The Key to Success

"We curate the data mixture to cover diverse domains that require reasoning, and a reject sampling procedure to improve the data quality. We then rewrite QwQ traces with GPT-4o-mini into a well-formatted version, inspired by Still-2, to improve data quality and ease parsing," the team says of their data preparation process in the blog.

Outperforming OpenAI’s o1-Preview

The model performed at or above o1-preview’s level on math and coding benchmarks but did not surpass o1 on the graduate-level benchmark GPQA-Diamond, which includes more advanced physics-related questions. NovaSky open-sourced all parts of the model, including weights, data, infrastructure, and technical details.

A More Affordable Reasoning Model

The relatively short 19-hour training time means Sky-T1 cost just $450 to build, according to Lambda Cloud pricing, the team clarifies in the blog post. Considering GPT-4 used a suspected $78 million in compute, it is no small feat to present an example of a more affordable reasoning model that can be replicated by academic and open-source groups that lack OpenAI’s funding.

Conclusion

The continued development of open-source AI models like Sky-T1-32B-Preview holds great promise for democratizing AI and creating a more even playing field for smaller labs, nonprofits, and other entities to develop competitive models. As the field of AI continues to evolve, it will be exciting to see how these open-source models can be used to drive innovation and progress.

FAQs

Q: What is the significance of Sky-T1-32B-Preview?
A: Sky-T1-32B-Preview is an open-source reasoning model that performs comparably to OpenAI’s o1-preview, built in just 19 hours for under $450 using eight Nvidia H100 GPUs.

Q: How was Sky-T1-32B-Preview developed?
A: The team developed Sky-T1 by fine-tuning Alibaba’s Qwen2.5-32-Instruct and trained it on data generated with QwQ-32B-Preview, another open-source model comparable to o1-preview.

Q: Is Sky-T1-32B-Preview available for use?
A: Yes, NovaSky open-sourced all parts of the model, including weights, data, infrastructure, and technical details.

Q: What are the potential applications of Sky-T1-32B-Preview?
A: The potential applications of Sky-T1-32B-Preview are vast, including but not limited to, natural language processing, computer vision, and robotics.

Data Lakehouses Primed for Explosive Growth

The Data Lakehouse: A Middle Ground for Data Management

The humble data lakehouse emerged about eight years ago as organizations sought a middle ground between the anything-goes messiness of data lakes and the locked-down fussiness of data warehouses. The architectural pattern attracted some followers, but the growth wasn’t spectacular. However, as we kick off 2025, the data lakehouse is poised to grow quite robustly, thanks to a confluence of factors.

The Rise of Data Lakes

As the big data era dawned back in 2010, Hadoop was the hottest technology around, as it provided a way to build large clusters of inexpensive industry-standard X86 servers to store and process petabytes of data much more cheaply than the pricey data warehouses and appliances built on specialized hardware that came before them.

The Need for a Middle Ground

By allowing customers to dump large amounts of semi-structured and unstructured data into a distributed file system, Hadoop clusters garnered them the nickname “data lakes.” Customers could process and transform the data for their particular analytical needs on-demand, or what’s called a “structure on read” approach. This was quite different than the “structure on write” approach used with the typical data warehouse of the day.

The Emergence of Data Lakehouses

As the Hadoop experiment progressed, many customers discovered that their data lakes had turned into data swamps. While dumping raw data into HDFS or S3 radically increased the amount of data they could retain, it came at the cost of lower quality data. Specifically, Hadoop lacked the controls that allowed customers to effectively manage their data, which led to lower trust in Hadoop analytics.

The Solution: Data Lakehouses

By the mid-2010s, several independent teams were working on a solution. The first team was led by Vinoth Chandar, an engineer at Uber, who needed to solve the fast-moving file problem for the ride-sharing app. Chandar led the development of a table format that would allow Hadoop to process data more like a traditional database. He called it Hudi, which stood for Hadoop upserts, deletes, and incrementals. Uber deployed Hudi in 2016.

The Rise of Table Formats

A year later, two other teams launched similar solutions for HDFS and S3 data lakes. Netflix engineer Ryan Blue and Apple engineer Daniel Weeks worked together to create a table format called Iceberg that sought to bring ACID-like transaction capabilities and rollbacks to Apache Hive tables. The same year, Databricks launched Delta Lake, which melded the data structure capabilities of data warehouses with its cloud data lake to bring a “good, better, best” to data management and data quality.

The Impact of Polaris and Tabular

The battle between Apache Iceberg and Delta Lake for table format dominance was at a stalemate. Then in June of 2024, Snowflake bolstered its support for Iceberg by launching a metadata catalog for Iceberg called Polaris (now Apache Polaris). A day later, Databricks responded by announcing the acquisition of Tabular, the Iceberg company founded by Blue, Weeks, and former Netflix engineer Jason Reid, for between $1 billion and $2 billion.

The State of the Data Lakehouse

Seven months later, that momentum is still going strong. Last week, Dremio published a new report, titled “State of the Data Lakehouse in the AI Era,” which found growing support for data lakehouses (which are now considered to be Iceberg based, by default).

Conclusion

The data lakehouse is poised to grow quite robustly in 2025, thanks to a confluence of factors. The rise of open, Iceberg-based lakehouse platforms is giving enterprises the freedom to choose the best query engine for their specific needs, rather than being locked into monolithic cloud platforms. As the data architecture landscape continues to evolve, the demand for data lakehouses will only continue to grow.

FAQs

Q: What is a data lakehouse?
A: A data lakehouse is a middle ground between the anything-goes messiness of data lakes and the locked-down fussiness of data warehouses.

Q: What are the benefits of a data lakehouse?
A: Data lakehouses provide a scalable and affordable way to store and process large amounts of data, while also providing the controls and governance needed to ensure data quality and trust.

Q: What are the key players in the data lakehouse market?
A: The key players in the data lakehouse market include Databricks, Snowflake, and AWS.

Q: What is the future of data lakehouses?
A: The future of data lakehouses is bright, with growing support for open, Iceberg-based lakehouse platforms and increasing demand for data lakehouses in the enterprise.

Microsoft Forms New Internal Dev-Focused AI Org

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Microsoft Creates New Engineering Organization to Accelerate AI Development

Microsoft has established a new engineering organization aimed at accelerating AI infrastructure and software development within the company.

Leadership and Structure

According to Bloomberg, Jay Parikh, previously VP and global head of engineering at Meta, will lead the new division. He will report to Microsoft CEO Satya Nadella and oversee groups, including the company’s AI platform and developer teams.

About the Leader

Parikh worked on technical infrastructure and data center projects at Meta. Before joining Microsoft in October, he was appointed the CEO of cloud security startup Lacework.

New Organization Structure

The new org, called CoreAI — Platform and Tools, is actually a combination of Microsoft’s existing Dev Div and AI platform teams, The Verge reports, along with some employees in the Office of the CTO division. CoreAI effectively rejiggers Microsoft’s developer teams to ensure AI remains a top priority.

CEO’s Vision

In an internal memo published on Microsoft’s blog, Nadella said that Microsoft’s focus for the coming year will be “model-forward” applications that “reshape all application categories.”

Conclusion

Microsoft’s new engineering organization, CoreAI — Platform and Tools, aims to accelerate AI infrastructure and software development within the company. Led by Jay Parikh, the organization will oversee groups, including the company’s AI platform and developer teams, to ensure AI remains a top priority. With this new structure, Microsoft is poised to focus on “model-forward” applications that will reshape all application categories.

Frequently Asked Questions

Q: What is the purpose of the new engineering organization?

A: The purpose of the new organization, CoreAI — Platform and Tools, is to accelerate AI infrastructure and software development within Microsoft.

Q: Who will lead the new organization?

A: Jay Parikh, previously VP and global head of engineering at Meta, will lead the new organization.

Q: What is the focus of Microsoft’s AI development efforts?

A: Microsoft’s focus for the coming year will be on “model-forward” applications that “reshape all application categories.”

Q: What changes are being made to Microsoft’s developer teams?

A: The new organization, CoreAI — Platform and Tools, effectively rejiggers Microsoft’s developer teams to ensure AI remains a top priority.

Wanted: Humans to Build Robots for OpenAI

OpenAI’s Robotics Department: A New Era in AI-Driven Hardware

OpenAI’s Robotics Department: A Brief History

Following the disbanding and reinstating of OpenAI’s robotics department over the past years and reports of OpenAI building its own robot, a series of new job listings on the robotics team suggest the company is finally ready to leap into hardware.

Job Listings and New Roles

Last Friday, Caitlin Kalinowski, who joined OpenAI in November to lead the robotics and consumer hardware team, shared the first OpenAI Robotics hardware roles via an X post. These job postings include an EE Sensing Engineer, Robotics Mechanical Design Engineer, and TPM Manager.

Robotics Team Description

Clicking links to the roles yields a description of a Robotics team that is "focused on unlocking general-purpose robotics and pushing towards AGI-level intelligence in dynamic, real-world setting." The description also describes the team’s aim to bring robotic systems from prototype to full-scale production.

Concerns and Benefits

Many users are expressing unease about OpenAI delving into the robotics space. However, when one user commented, "The thought of OpenAI with hardware😬", Kalinowski attempted to diffuse concerns, citing the benefits instead.

"Yeah, I cannot wait!" replied Kalinowski. "Imagine being able to send a robot to do a job that’s really dangerous for a human."

Integration into Physical Devices

Until now, OpenAI has developed its AI models to streamline workflows, with the next logical step being integration into physical devices to enhance user assistance. However, following examples like Elon Musk’s Tesla, which announced the Optimus humanoid robot in 2021 but has yet to release it, a market-ready robot may still be years away.

Focus and Future Plans

Robotics can also mean things outside of humanoid robots, including less flashy robots — such as robot vacuums — that help accomplish everyday functions. Moreover, when asked in the comments if the focus would be humanoid robots, Kalinowski said that is yet to be determined, with the initial team defining and building the roadmap.

Conclusion

OpenAI’s foray into robotics marks a significant step forward in the company’s mission to integrate AI into everyday life. With a focus on unlocking general-purpose robotics and pushing towards AGI-level intelligence, the company is poised to make a significant impact in the robotics industry.

FAQs

Q: What are the job listings for OpenAI’s robotics department?
A: The job listings include an EE Sensing Engineer, Robotics Mechanical Design Engineer, and TPM Manager.

Q: What is the focus of OpenAI’s robotics team?
A: The focus is on unlocking general-purpose robotics and pushing towards AGI-level intelligence in dynamic, real-world setting.

Q: When can we expect to see a market-ready robot from OpenAI?
A: It may take years, as seen in examples like Elon Musk’s Tesla, which announced the Optimus humanoid robot in 2021 but has yet to release it.

Q: Will OpenAI’s focus be on humanoid robots?
A: That is yet to be determined, with the initial team defining and building the roadmap.

Mercedes-Benz Virtual Assistant uses Google’s Conversational AI

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Mercedes-Benz Unveils Enhanced AI-Powered Virtual Assistant

New MBUX Virtual Assistant: A Game-Changer in the Automotive Industry

When Mercedes-Benz revealed its new AI-powered virtual assistant at CES in 2024, it didn’t specify which company’s Language Model it was running on. However, the existing MBUX Voice Assistant system, which can handle about 20 commands triggered with "Hey Mercedes," now includes results provided by OpenAI’s ChatGPT and Microsoft Bing. But it’s not a conversational platform. According to Mercedes, there’s a plan to roll out this upgraded system to "further models" that run the older Voice Assistant, but it didn’t specify which ones.

What’s New in the MBUX Virtual Assistant?

The new MBUX Virtual Assistant will feature four "personality traits," including natural, predictive, personal, and empathetic. It can also ask you questions for additional clarity to get you what you need.

Google’s AI Agent: A Tailor-Made Solution for the Automotive Industry

Google’s new AI Agent is designed specifically for automotive uses, leveraging Google Maps data to find points of interest, look up restaurant reviews for you, give you recommendations, answer follow-up questions, and more. Google says MBUX Virtual Assistant users will get access to "nearly real-time" Google Maps updates. It also says it can "handle complex, multi-turn dialog."

The Technology Behind the Agent

The agent uses Gemini and runs on Google Cloud’s Vertex AI development platform, designed to help companies build out AI experiences. "This is just the beginning of how agentic capabilities can transform the automotive industry," Google CEO Sundar Pichai stated in a press release.

Conclusion

The new MBUX Virtual Assistant is expected to revolutionize the automotive industry with its advanced AI-powered capabilities. With its ability to understand and respond to natural language, it will provide a more intuitive and user-friendly experience for drivers. The partnership with Google’s AI Agent will further enhance the capabilities of the MBUX Virtual Assistant, offering a more comprehensive and personalized experience for users.

FAQs

Q: What is the new MBUX Virtual Assistant?
A: The new MBUX Virtual Assistant is an AI-powered virtual assistant designed to provide a more intuitive and user-friendly experience for drivers.

Q: What are the features of the new MBUX Virtual Assistant?
A: The new MBUX Virtual Assistant features four "personality traits," including natural, predictive, personal, and empathetic, and can ask questions for additional clarity to get you what you need.

Q: What is the partnership between Mercedes-Benz and Google?
A: The partnership is between Mercedes-Benz and Google, which will integrate Google’s AI Agent into the new MBUX Virtual Assistant, providing users with access to "nearly real-time" Google Maps updates and other features.

Q: When will the new MBUX Virtual Assistant be available?
A: The new MBUX Virtual Assistant is expected to be rolled out to "further models" that run the older Voice Assistant, but it didn’t specify which ones.