During the online audio event, which was streamed live on the social media platform, Musk’s all-male team of mathematics, artificial intelligence and engineering specialists talked about creating an artificial intelligence that is smarter than humans in order to benefit society.

The focus on Musk and his team’s idea is not on a competitive note. They have no intentions of competing with OpenAI, but rather are focused on answering deep scientific questions.

Musk also said he sees xAI as an alternative to larger AI companies, but said it is still “in its infancy” and that it will take time to catch up to OpenAI and Google.

Questions about the nature of dark matter as well as unsolved aspects of the workings of gravity may be among the mysteries his company is trying to explain. Musk spoke at length about the “Fermi paradox,” a theoretical question that asks why humans have not yet discovered extraterrestrials and suggests that technological civilizations typically self-destruct or are destroyed by an outside force before heading to other solar systems.

Jimmy Ba, a University of Toronto professor and well-known AI researcher who joined Musk’s company, said the goal is to create an AI that can help with a range of problems facing humanity.

“How can we build a general purpose problem-solving machine to help all of us, humanity, overcome the most challenging and ambitious problems out there, and how can we use those tools to augment ourselves and empower,” said Jimmy Ba.

Musk said his company’s artificial intelligence could offend people because it would speak “the truth.”

During the Twitter event, Musk said his company will need computing power, but it won’t be as big as other companies and the team will be relatively small.

Python vs Julia

Julia is a multi-paradigm, mostly functional programming language designed for machine learning and statistical programming. Python is another multi-paradigm programming language used for machine learning, although Python is generally considered object-oriented.

The new noise in the IT sector – will it die down soon? 

On the other hand, Julia is based more on the functional paradigm. This language is the new noise in the IT sector, mostly recognized for its speed, and is gaining popularity among data scientists.

The Question

Python vs Julia: which programming language should data professionals be learning in 2023?

The benefits of Python

Created in 1991, Python is a programming language that is used for website development, software development, mathematics, and systematic scripting. In Python, the first element of an array is accessed with null, such as string [0] in Python for the first character in a string. This helps by way of adoption by a more general-purpose audience with ingrained programming habits.

Python is popular among developers because of its power, adaptability and understandable syntax that is easy to understand and master. Nearly 70% of developers say they use Python to build high-performance AI and ML algorithms for natural language processing and sentiment analysis. Python, along with R, is the preferred language for Data Science. The breadth and utility of Python’s third-party package culture remains one of the language’s biggest attractions, noted Analytics Insight.

Last but not least, Python is easier to accelerate. The mypyc project translates type-annotated Python to native C, much less clumsily than Cython. Typically, a fourfold performance improvement is achieved, and often much more for purely mathematical operations.

The benefits of Julia 

Julia is a high-level, high-performance dynamic programming language. Many of its features are suitable for numerical analysis and computational science. Julia’s JIT compilation and type declarations mean that it can regularly outperform “pure”, unoptimized Python by orders of magnitude. Julia was designed to be faster from the start.

Julia’s primary target audience is users of scientific computing languages and environments such as Matlab, R, Mathematica, and Octave. The syntax of mathematical operations in Julia more closely resembles the way mathematical formulas are written outside the world of computers, making it easier for non-programmers to adopt.

Flux is a machine learning library for Julia that has many existing models for common use cases. Because it’s written entirely in Julia, it can be tweaked to suit the user’s needs and leverages Julia’s native just-in-time compilation to optimize projects from the inside out, Analytics Insight notes.

Julia is a high-level, high-performance dynamic programming language designed primarily for technical computing. The main advantages of Julia when dealing with complex data models are its simplicity, excellent performance and speed. But potential for converting Science’s formulaic language into code is an obstacle for scientists: Julia supports the use of Greek letters, which allows the direct use of mathematical formulas in code rather than translating such recipes into the coding language.

Galactica’s architecture is based on a transformer, an attention mechanism which draws global dependencies between input and output.

Some of the changes compared with the original transformer include using  GeLU as an activation function, learnt position embedding,  a vocabulary using byte pair encoding method and no bias parameter on dense-kernel or layer-norms.

The researchers trained the model using a tokenization process with various modalities like natural language versus math formulas versus molecular sequences, etc.

The source material for the dataset included 48-million papers, textbooks, reference materials, compounds, proteins and other sources of scientific knowledge. They implemented a special token to identify sections of step-by-step reasoning, which encourages Galactica to apply an internal working memory of sorts, which it would otherwise not be able to do.

Galactica has a few limitations. It can trend toward using toxic language, a behavior known as “hallucination”. Other limitations are frequency bias and overconfidence, especially about highly specialized scientific content.

Omniverse now supports batch workloads that AI and HPC researchers, scientists and engineers can run on their existing A100 or H100 systems — including rendering videos and images or generating synthetic 3D data.

“Today’s scientific computing workflows are extremely complex, involving enormous datasets that are impractical to move and large, global teams that use their own specialized tools,”

said Dion Harris, lead product manager of accelerated computing at NVIDIA.

“With new support for Omniverse on A100 and H100 systems, HPC customers can finally start to unlock legacy data silos, achieve interoperability in their complex simulation and visualization pipelines, and generate compelling visuals for their batch-rendering workflows.”

he continued. 

Using Omniverse and hybrid-cloud workloads, scientific computing customers can connect legacy simulation and visualization pipelines to achieve distributed, fully interactive, true real-time interaction with their models and datasets.

The billionaire entrepreneur tweeted that the technology will “also be important for Mars”. On Monday, Musk published on the social media:

“SpaceX is starting a program to take CO2 out of atmosphere & turn it into rocket fuel. Please join if interested”.

The statement came days after the SpaceX and Tesla founder was named Time magazine’s person of the year.

A “clown, genius, edgelord, visionary, industrialist, showman” in its piece – that’s how the magazine called him.

The Financial Times newspaper also named him as their person of the year on Wednesday.

Amazon founder Jeff Bezos was surpassed by Musk`s nomination as the world’s wealthiest person this year.

There is currently no further information on the carbon capture plans from SpaceX.

It is not yet clear how SpaceX would capture carbon and turn it into rocket fuel. Scientific processes do exist, such as the one used by Canadian company Carbon Engineering, which is making liquid fuel with carbon dioxide from the atmosphere which is then combined with hydrogen from water.

Dev environments will move to the Cloud. Local development environments are now largely the only part of the software development lifecycle time that is done locally on a developer’s computer. Automated builds, staging environments and running production applications have largely moved from local computers to the cloud.

DevOps will become more scientific. Google’s DORA (DevOps Research and Assessment) team has conducted research which found that companies with elite performing engineering organisations are twice as likely to achieve their organisational goals and achieve a 50% higher growth rate over three years.

To deliver new functionality quickly and reliably without developers burning out, software development teams have to ensure their processes and tools are as polished as they can be.

Remote work will be permanent. GitHub’s 2021 State of the Octoverse report found that while 41% of respondents were co-located in an office before the pandemic, only 10.7% expected to remain in the office after the pandemic. This represents a 74% decrease in the rate of co-located office working.

The report also found that developers expect a 41% increase in the rate of hybrid working, where some staff work fully remotely and some come into the office. Companies employing fully remote working were expected to increase by 46% compared with before the pandemic.

Built on AMD CDNA™ 2 architecture, AMD Instinct MI200 series accelerators deliver application performance for a broad set of HPC workloads. Forrest Norrod, senior vice president and general manager, Data Center and Embedded Solutions Business Group at AMD, commented:

“AMD Instinct MI200 accelerators deliver leadership HPC and AI performance, helping scientists make generational leaps in research that can dramatically shorten the time between initial hypothesis and discovery. With key innovations in architecture, packaging and system design, the AMD Instinct MI200 series accelerators are the most advanced data center GPUs ever, providing exceptional performance for supercomputers and data centers to solve the world’s most complex problems.”

Exascale With AMD

AMD designed the Frontier supercomputer in collaboration with the U.S. Department of Energy, Oak Ridge National Laboratory, and HPE. The supercomputer is expected to deliver more than 1.5 exaflops of peak computing power. Frontier is created to push the boundaries of scientific discovery by enhancing AI performance, analytics, and simulation at scale. This would help scientists to pack in more calculations, identify new patterns in data, and develop innovative data analysis methods to accelerate the pace of scientific discovery. Thomas Zacharia, director at Oak Ridge National Laboratory, explained:

“The Frontier supercomputer is the culmination of a strong collaboration between AMD, HPE and the U.S. Department of Energy, to provide an exascale-capable system that pushes the boundaries of scientific discovery by dramatically enhancing performance of artificial intelligence, analytics, and simulation at scale.”

Powering The Future of HPC

The AMD Instinct MI200 series accelerators, combined with 3rd Gen AMD EPYC CPUs and the ROCm™ 5.0 open software platform, are designed to propel new discoveries for the exascale era and tackle our most pressing challenges from climate change to vaccine research.

Key capabilities and features of the AMD Instinct MI200 series accelerators 

AMD CDNA™ 2 architecture presents second Gen Matrix Cores accelerating FP64 and FP32 matrix operations, delivering up to 4X the peak theoretical FP64 performance compared to AMD previous-gen GPUs.

Leadership Packaging Technology is a multi-die GPU design with 2.5D Elevated Fanout Bridge (EFB) technology that delivers 1.8X more cores and 2.7X higher memory bandwidth compared to AMD previous-gen GPUs. It offers aggregate peak theoretical memory bandwidth at 3.2 terabytes per second.

3rd Gen AMD Infinity Fabric™ technology offers up to 8 Infinity Fabric links that connect the AMD Instinct MI200 with 3rd Gen EPYC CPUs and other GPUs in the node to enable unified CPU/GPU memory coherency and maximize system throughput. This allows for an easier on-ramp for CPU codes to tap the power of accelerators.

Software for Enabling Exascale Science

AMD ROCm is an open software platform allowing researchers to tap the power of AMD Instinct accelerators to drive scientific discoveries. The ROCm platform is built on the foundation of open portability, supporting environments across multiple accelerator vendors and architectures. With ROCm 5.0, AMD extends its open platform powering top HPC and AI applications with AMD Instinct MI200 series accelerators, increasing accessibility of ROCm for developers and delivering leadership performance across key workloads.

Through the AMD Infinity Hub, researchers, data scientists, and end-users can find and install containerized HPC apps and ML frameworks that are optimized and supported on AMD Instinct accelerators and ROCm. The hub currently offers a range of containers supporting Radeon Instinct MI50, AMD Instinct™ MI100, or AMD Instinct MI200 accelerators. They include several applications like Chroma, CP2k, LAMMPS, NAMD, OpenMM, and more, along with ML frameworks, such as TensorFlow and PyTorch. New containers are continually being added to the hub.

Cristina Bedda, Software Developer at Luxoft, Italy

Cristina Bedda has been a Software Developer at Luxoft, Italy, for almost a year and a half. She obtained her PhD in Physics and after years of experience as an experimental physicist at CERN, she decided to switch her professional pathway and move to the IT industry. She has always been interested in scientific and computer technology subjects. As a physicist, Cristina started to develop code to analyze the data from the experiment she was working on and to test hardware functionalities. When she decided to change the industry, Cristina realized coding was the part of her job that she enjoyed the most. Therefore, she decided to apply for IT positions, even though she knew that she would have to learn a lot.

Cristina thinks it is very important to keep the discussion about having a diverse and inclusive workplace going. She said that a lot of work in this direction has been done in the last few years, but there is still more to do to overcome stereotypes. She hopes that she can be part of this change to see more women in the IT industry soon. In the past, Cristina has been struggling to keep the right balance between her personal and professional life. She worked until her tasks were entirely completed, neglecting her personal life which caused her a lot of stress. Nowadays Cristina knows that if we want to stay proactive we have to leave our brains to rest and disengage ourselves from work. In that way, we can restart the next day with a fresh and clear picture.

Cristina advises everyone not to be afraid to go for a career in the IT industry if it is something that we are really interested in. She tells us not to listen to our inner voice if it is saying that we are not good enough. Cristina thinks that instead of doing this we have to look back at what we have already achieved, and motivate ourselves even more. Cristina adds that projects in the IT field can change very fast, and everyone is learning new stuff every day, so we have to keep a positive and proactive attitude all the time. She shares that she has had the chance to work with very confident and passionate women who were not afraid to speak up and raise their voices when it is needed. In fact, these females were the ones who really inspired Cristina to gain more self-confidence and overcome her inner insecurity. Nowadays, Cristina hopes that everyone will find their inspiration to develop and succeed in both personal and professional plans.

Can you tell us about yourself and your experience?

My name is Cristina, and I’ve been a software developer at Luxoft Italy for almost a year and a half. I have a PhD in physics and, after a few years in academia working as an experimental physicist at CERN, I decided to move to the IT industry.

What influenced you to start a career in IT?

I have always been interested in scientific and computer technology subjects. As a physicist, I started to develop a code to analyze the data from the experiment I was working on and started testing hardware functionalities. When I decided to change the industry, I realized coding was the part of the job that I enjoyed. Therefore, I decided to apply for IT positions, even though I knew that I would have to learn a lot!

Read More Inspiring Stories for Women in Tech in WRTech here.

What inspired you to become an ambassador?

I think it is very important to keep the discussion about having a diverse and inclusive workplace going. A lot of work in this direction has been done in the last few years, but there is still more to do to overcome stereotypes, and I hope I can be part of the change. I really hope to see more women in IT soon and that people (including me) start recognizing the role of unconscious biases in their decisions.

What advice would you give to people interested in raising their awareness of D&I?

I would suggest taking some of the tests related to unconscious biases that can be easily found online. For example, I took one related to the association between gender and humanities and scientific careers, and it made me really think about the subject and how hard it is to get over
our inner biases.

What advice would you give to all young women considering a career in IT?

I would just say to not be afraid and go for it if it is something that you are interested in. Don’t listen to the inner voice saying that you are not good enough and instead look back at what you achieved. From my still limited experience in IT, projects can change very fast, and everyone is learning new stuff every day, so just keep a positive and proactive attitude.

“When I decided to change the industry, I realized coding was the part of the job that I enjoyed, therefore I decided to apply for IT positions, even though I knew that I would have to learn a lot!”

How do you manage your professional and personal lives to be successful in both?

In the past, I struggled to keep a balance between my personal and professional lives. I couldn’t define a border between them, and I would just work until my task was completed, neglecting my peрsonal life. Of course, that leads me to a lot of stress, and I learned that, most of the time, it is better to leave your brain the time to rest and disengage from work. It allows you to restart the next day with a fresh, clear picture.

Who is a role model for you? Who do you look up to and why?

There are a lot of stories about successful women and men that can inspire us, but I don’t think it is very easy to connect ourselves to role models that are very far from our daily lives. I’ve had the chance to work with very confident and passionate women that were not afraid to speak up and raise their voices when it was needed. They really inspired me to follow in their footsteps and overcome my inner insecurity. I really hope everyone can find someone to inspire themselves like this.

Favourite clothes?

Jeans and a Т-shirt.

High heels or flat shoes?

Low shoes absolutely.

Which typical female quality or household responsibility does not apply to you?

Cleaning.

What is THE thing that you must have in your bag when going out?

My phone.

Favourite lipstick colour?

I don’t wear makeup often, maybe light red.

If You Want to Read More Inspiring Stories for Success and Professional Growth Join the DevStyleR #Tech Influencers Community and Get Access to our Special Editions here.