The report makes a compelling case for the adoption of memory-safe programming languages.

This new focus builds on the goal of restoring cybersecurity responsibility and restructuring incentives in favor of long-term cybersecurity investments. Memory-safe programming languages were also included as a goal of the Open Source Software Security Initiative (OS3I), which recently published a new report.

When using a memory-safe programming language such as Rust, Go, Java, Swift, and Python, developers cannot create code that causes a memory error because the language includes specific properties such as memory safety. When developers write code in languages that are not memory safe, such as C and C++, they may inadvertently write code that can cause memory access errors. Instead of the errors being caught at compile time and runtime, as with memory-safe languages, they end up in the final version and cause security problems, writes Jennifer Gregory, an author on cybersecurity topics.

Gregory adds that choosing a memory-safe language significantly reduces or completely eliminates memory-related vulnerabilities. In addition to improved security, memory-safe languages also reduce crashes and allow developers to increase productivity because they don’t need to focus on memory management issues.

The report focuses on getting organizations to focus on two specific areas related to memory-safe languages. In addition, the government wants to focus on creating partnerships with the technical community, especially engineers and developers, to collaborate on making this key change happen.

Although the use of memory-safe languages is recommended, the transition to them is challenging. Many software programs and libraries are based on inappropriate memory protection languages, and rewriting the entire database is often impossible. Starting a new project with a safe programming language represents the easiest way to transition.

Organizations can reduce attacks by rewriting only the critical functions and libraries that are most vulnerable to memory-related errors. Suitable languages such as Rust and Swift are interoperable with C and C++, making this transition easier. However, making the transition requires adequate developer resources. Organizations should start by assessing their existing memory safety language experts and provide training for both current and new developers in this area.

La inversión de @Microsoft en España se va a cuadruplicar y llegará hasta los 1.950 M€.

Quiero agradecer a su presidente, @BradSmi, su confianza en la economía española y en nuestra hoja de ruta para una transformación digital inclusiva y segura.

Además, hemos analizado las… pic.twitter.com/0PG1tZL08e

— Pedro Sánchez (@sanchezcastejon) February 19, 2024

Smith said it was not just about building data centres, but a commitment to help develop the “security, development and digital transformation of the country’s government, businesses and people”.

He added that the tech giant has been investing in Spain for 37 years. In September 2021, the company announced that a new artificial intelligence technology research and development centre would be established in Barcelona. It was then that Alberto Granados, president of Microsoft Spain, said that Microsoft’s investment is proof that Spain has the necessary potential to develop the digital sector.

This is the second major investment Microsoft has made in Europe within a month. Recall that last week the company invested 3 billion euros in the development of the artificial intelligence ecosystem in Germany.

Similar to its plans in Spain, over the next two years Microsoft has pledged to invest the funds in strengthening the AI infrastructure in Germany, building new data centres and training people to develop AI skills.

Major technology companies are increasing their presence in Europe by investing in the development of artificial intelligence, which in turn coincides with the implementation of EU legislation on artificial intelligence.

Read more:
1. European Commission Investigates TikTok
2. Equinix Announces Fully Managed Service for NVIDIA DGX AI Supercomputing
3. Android Releases First Preview of Android 15 for Developers

Princeton University

Samsung Research America (SRA) has joined Princeton University‘s new NextG Corporate Affiliates program, reports Telecoms News. The program brings together industry and academia for the sake of research in 6G wireless networks and related technologies.

“The best academic research to help realise the promise of future wireless networks is informed by close interactions between faculty and industry technology leaders. Our NextG program aims to foster those deep collaborations around wireless technology, as well as policy, that are required to drive meaningful innovation and global leadership in an era of rapid change”, said Andrea Goldsmith, Dean of Princeton’s School of Engineering and Applied Science.

The NextG initiative was launched last year at Princeton’s School of Engineering and Applied Science and aims to increasingly advance innovation in networking, sensing and other key areas that will be the foundation of next-generation data infrastructure.

“We encourage Princeton’s researchers to continue innovating and develop potentially disruptive 6G technologies,” said Dr Charlie Zhang, SVP at SRA.

With SRA chairing key 6G standardization groups, the company is now in a position to quickly translate Princeton’s insights into broader ecosystem developments.

With the new partnership, Samsung aims to take wireless networks into a new era in which 6G promises significantly higher speeds, lower latency and expanded opportunities for new mobile applications.

Read more:
1. GitHub Opens New Group for AI-Focused Open Source Projects
2. Voltron Data Releases Ibis 8.0, Developers Will Write Code Only Once
3. CEO’s Amazon Prime: “I Have no Plans to Adjust the Prime Team”

Image: Allen Institute for AI

The Allen Institute for Artificial Intelligence (AI2) has released OLMo, an open large language model that aims to help better understand what happens in AI model processes, as well as contribute to developments in the field of language model science.

Allen’s collaboration with the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University, as well as partners including AMD, CSC-IT Center for Science (Finland), Paul G. Allen School of Computer Science & Engineering at the University of Washington, and Databricks are making the OLMo project a reality.

OLMo is being released along with pre-training data and training code that, as the institute’s announcement says, “is not available today in any open model of this scale.”

Among the development tools in the framework are pre-training data built on AI2’s Dolma set, which includes three trillion tokens, along with the code that creates the training data.

“Many language models today are published with limited transparency. Without access to training data, researchers cannot scientifically understand how a model works. This is akin to discovering drugs without clinical trials or studying the solar system without a telescope,” says Hannah Hajishirzi, OLMo project leader, senior director of NLP Research at AI2, and professor at UW’s Allen School.

He adds that thanks to OLMo, researchers “will finally be able to study the science of LLM, which is critical to building the next generation of safe and reliable artificial intelligence.”

The Allen Institute for Artificial Intelligence noted that OLMo provides researchers and developers with greater accuracy by offering insights into the training data behind the model, removing the need to rely on assumptions about how the model works. And because models and datasets are open, researchers can learn and build on previous models and work.

In the coming months, AI2 will continue to iterate on OLMo and will bring different model sizes, modalities, datasets, and capabilities into the OLMo family.

In this evolving landscape, a background in programming or coding may not be the sole prerequisite for individuals aspiring to shape the automated future. According to Candy, a fundamental grasp of language and its application could be the key to securing lucrative AI-related positions.

“Rather than us having to learn to talk the language of technology and programming computers, effectively they’re learning to talk our language,” Candy told Fortune.

Candy’s statement comes after a dramatic increase in demand for engineers who feed large language models like ChatGPT and Bard with questions and information to train it on human behavior and thinking.

Pay for the “professions of the future” can reach six-figure sums and are typically not dependent on IT skills.

As language models such as ChatGPT face challenges in terms of accuracy and can produce false information – known as ‘hallucinations’, the need for people with good language skills to train and interact with chatbots is becoming increasingly apparent.

In May, IBM’s CEO announced a reduction in hiring for certain roles, particularly within HR, where the majority of staff interactions now occur with a bot, according to Candy.

Addressing the Fortune Brainstorm AI conference, Paul Daugherty, Accenture’s CTO, discussed the anticipated “consolidation” of the workforce due to AI expansion, implying a decreased need for personnel to perform repetitive tasks.

“The biggest worry is the jobs for the people who won’t be using generative AI,” Daugherty said.

IBM’s Candy agrees that while he doesn’t see AI as a de facto replacer of jobs, it is likely that people who can’t use the technology will be replaced by those who can.

According to the company, the typical approach to creating a multimodal model involves training components for different formats of information separately and then combining them together. What sets Gemini apart is that it is trained for different formats from the start and then refined with additional multimodal data.

“This helps Gemini seamlessly understand and reason about all kinds of inputs from the ground up, far better than existing multimodal models — and its capabilities are state of the art in nearly every domain,” Sundar Pichai, CEO of Google and Alphabet, and Demis Hassabis, CEO and co-founder of Google DeepMind, wrote in a blog post.

Google also explained that the new model has quite sophisticated reasoning capabilities that allow it to understand complex written and visual information, making it “adept at discovering knowledge that can be difficult to discern among vast amounts of data.”

For example, he can read hundreds of thousands of documents and extract this information to lead to new discoveries in certain fields.

Its multimodal nature makes it particularly suited to understanding and answering questions in complex fields such as mathematics and physics.

Gemini 1.0 offers three distinct versions to cater to various size preferences: Ultra, Pro, and Nano, listed in descending order of size.

According to Google’s initial benchmarking of Gemini, the Ultra version has demonstrated superior performance, outperforming 30 out of the 32 widely used academic benchmarks in model development and research. Notably, Gemini Ultra has achieved a milestone by surpassing human expert performance in massive multitask language understanding (MMLU), encompassing 57 subjects such as math, physics, history, law, medicine, and ethics.

The integration of Gemini Pro into Bard marks a significant milestone, constituting the most substantial update to Bard since its initial launch. The Pixel 8 Pro now leverages Gemini Nano to enhance functionalities like Summarize in the Recorder app and Smart Reply in Google’s keyboard.

Over the coming months, Gemini is set to extend its presence to additional Google products, including Search, Ads, Chrome, and Duet AI.

Starting from December 13, developers can access Gemini Pro through the Gemini API available in Google AI Studio or Google Cloud Vortex AI.

“We regularly review our businesses and make adjustments to center on long-term strategic growth areas. Following a recent review, we’ve made the difficult decision to restructure our gaming business,” a ByteDance spokesperson told TechCrunch in a statement.

The round of mass layoffs began today and many Nuverse employees are awaiting a decision on their future. It is unclear how many employees will be affected by the restructuring. The unit employs about 3,000 people, according to Chinese tech news publication LatePost.

The value of ByteDance’s spending in recent years on acquisitions is in the billions of dollars. This includes a $4 billion purchase of a Shanghai studio called Moonton. Earlier this month, Reuters reported that the company wanted to sell the studio and had met for discussions with another Saudi Arabian company.

ByteDance’s struggles in the video game industry, as well as its foray into virtual reality with Pico, raise questions about the broad effectiveness of its data-driven A/B testing approach that propelled TikTok to worldwide prominence. While ByteDance has accumulated an unparalleled wealth of consumer insights through its short video apps, the success of video games requires a more protracted and patient creative process, presenting a challenge distinct from the immediate satisfaction provided by dopamine-infused video clips. In contrast, competitors Tencent and NetEase are investing heavily in games with lengthier development cycles.

The extensive layoffs at Nuverse contribute to the ongoing challenges faced by the Chinese internet industry, which has been grappling with a pervasive regulatory crackdown in recent years. This has resulted in diminished business activities and workforce reductions across the sector, TechCrunch writes. The video gaming industry, in particular, experienced significant setbacks due to a pause in license approvals. Despite the resumption of the approval process, the sector’s recovery has been constrained by broader macroeconomic challenges.

“Neo4j has been an AWS Partner since 2013 – with this latest collaboration representing an essential union of graph technology and cloud computing excellence in a new era of AI,” said Sudhir Hasbe, the chief product officer at Neo4j.

“Together, we empower enterprises seeking to leverage generative AI to better innovate, provide the best outcome for their customers, and unlock the true power of their connected data at unprecedented speed”, he added.

The goal of this collaboration is to reduce the hallucinations associated with generative artificial intelligence by making the results more accurate, transparent and explainable. This partnership addresses a common challenge for developers working with LLM by providing a solution for creating long-term memory in LLM grounded in specific enterprise data and domains.

Neo4j’s fully managed graph database, Neo4j Aura Professional, is now accessible on AWS Marketplace. Tailored for generative AI developers, it provides a swift start. The graph database, with native vector search, excels at capturing explicit and implicit relationships, supporting the creation of knowledge graphs. This enhances AI systems’ reasoning and inference capabilities, positioning it as an enterprise database suitable for grounding LLMs and serving as long-term memory.

“At AWS, we remain committed to empowering organizations with a diversity of tools and resources to build generative AI solutions that align with their unique customer experiences, applications, and business requirements. With Neo4j’s graph database and Amazon Bedrock’s integration, we aim to provide customers sophisticated options to deliver more accurate, transparent, and personalized experiences for their end-users in a fully managed manner”, said Atul Deo, the general manager at Amazon Bedrock, AWS.

Neo4j is launching an integration with Amazon Bedrock, enabling seamless access to foundation models from top AI companies through an API. This integration aims to minimize AI hallucinations, enhance personalized experiences, provide comprehensive real-time search results, and initiate knowledge graph creation by structuring unstructured data for loading into a knowledge graph.

Despite the trend of layoffs by major tech companies, projections show that the tech market will recover faster than we even expect. Technology is constantly evolving, and computer professionals and those with a technical mindset are driving this revolution. The skills we acquire in computer science can lead to high-paying jobs in a variety of fields, such as software development, data analysis and artificial intelligence.

In this post, we will look at how opting for a career in computer science gives a professional a chance of a good and secure future with a high-paying job and a successful career, according to Dev Internships.

How To Get Rich From Computer Science

1. Work At A Large Tech Company
According to data from Glassdoor, the average base salary for a software developer at Google is around $120,000 per year, while the average base salary for a software developer at Facebook is around $130,000 per year. However, senior developers and team managers can earn a lot more.

Additionally, as a developer at one of these companies, you’ll receive bonuses and stock options as part of your compensation package which can greatly appreciate as the company grows in value. Considering the high salary, large bonuses, and stock options, you can quickly earn enough to become rich if you keep your living expenses low and manage your money well.

2. Work At A Start-Up
On the other end of the spectrum, working at a budding tech company that has potential can also make you rich. In fact, if you end up at the right company at the right time and stick it out, this can be a much better option than working at a large tech company. Imagine working at Facebook when it was small and helping it grow until it went public.

The main reason why this can be a better option (why it can make you even more money) than working at an established company is that the stock you would receive as compensation has more growth potential.

However, there is also more risk with this option if you take a good portion of your compensation from stock options. If the company doesn’t grow into the next unicorn and perhaps even fizzles out, then your stock would become worthless.

3. Grow A Large Tech YouTube Channel
One of my favorite YouTube channels is Tech With Tim. He recently reached over 1,000,000 subscribers on his channel which is amazing. However, when he had just under 400,000 subscribers, he showed how much money his channel made.

At that point, his channel made more than $7,000 per month in ad revenue alone. Tim admitted that this wasn’t even the majority of the money that he earned from his channel. He made most of his YouTube income from affiliate sales and sponsored videos. Now that his channel has over 1,000,000 subscribers, I would guess he earns more than $50,000 per month.

Additionally, Tim has participated in and started other businesses that he is able to jump-start simply by mentioning them to his large audience. If you want to start your own YouTube channel, there are a few ways to potentially monetize it:

• Advertising revenue: As your channel grows in popularity, you can apply to join the YouTube Partner Program, which allows you to monetize your videos through ads.
• Sponsorships: As your channel gains traction, you may be able to secure sponsorships or brand deals to promote products or services related to technology.
• Affiliate marketing: You can also earn money by including affiliate links in your video descriptions, which allow you to earn a commission on any sales made through those links.
• Creating and selling your own products: As your channel grows, you can leverage your audience to sell your own products or services, such as an online course, e-book, or merchandise.

4. Create Your Own Business
As a Computer Science graduate, this is my dream. And the best part is that it’s totally possible to get rich by creating your own tech company. It’s happening now more than ever. Especially now that A.I. has become ubiquitous, it’s not difficult to leverage the web and A.I. to start a business and become rich.

Here are a few examples of AI-based companies that you could potentially create:

• A healthcare AI company that utilizes machine learning algorithms to improve medical imaging and diagnostic accuracy.
• A retail AI company that uses natural language processing and computer vision to improve the online shopping experience.
• A financial AI company that uses predictive analytics to help businesses and individuals make better investment decisions.
• A transportation AI company that uses real-time data and machine learning to optimize delivery routes and reduce fuel consumption.
• A manufacturing AI company that uses machine learning to improve quality control and predict maintenance needs for manufacturing equipment.
• An HR AI company that uses natural language processing and machine learning to improve the recruitment and performance evaluation processes.
• A security AI company that uses computer vision and machine learning to improve surveillance and detect potential security threats.

These are just a few examples, but there are many other industries and applications that AI can be used for. It’s important to research and identify the area that you have more knowledge or interest in and that you see a potential opportunity to solve a problem or improve a process.

5. Invest In Tech
The last way to get rich from Computer Science is to be an investor. Sometimes, it takes money to make money. And if you have money, tech is a great place to put it if you want it to grow. Granted, the tech sector is always shifting so you have to stay on top of trends and current news or risk your money going the way of MySpace.

One way to invest in the tech sector is to buy stocks in technology companies that you believe will perform well in the future. You can do this by opening a brokerage account and buying individual stocks, or by investing in technology-focused exchange-traded funds (ETFs) or mutual funds.

Another option is to invest in venture capital funds that focus on technology startups, which can offer the potential for high returns but also come with a higher level of risk. It’s important to do your own research and consult a financial advisor before making any investment decisions.

“AI is slated to unlock nearly $16 trillion in productivity by 2030. With the launch of the IBM Enterprise AI Venture Fund, we’re opening another channel to harness the enormous potential of the AI revolution into tangible, positive outcomes for IBM and the companies we invest in. This fund is yet another way we’re doubling down on our commitment to responsible AI innovation through watsonx and helping organizations put this transformational technology to work”, said Rob Thomas, Senior Vice President, Software and Chief Commercial Officer, IBM.

IBM says its Enterprise AI Venture Fund approach is unique because it combines IBM’s domain expertise and a dedicated portfolio development team to invest in AI companies that are making a significant impact in the business world.

The fund will offer startups the chance to form partnerships with IBM, receive operational guidance, and enhance their product and engineering strategies. IBM, with its established history in enterprise AI, is leveraging its expertise and the latest generative AI innovations to expand its AI partnerships and create value for AI leaders and clients, both new and existing.