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9 February 2024
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Technology Trends for 2024

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The breakneck pace of technological advancements is causing a domino effect on change, impacting more than just technology itself. This evolving landscape forces IT professionals to acknowledge that their roles will fundamentally shift in a post-contactless world. To thrive in this new reality, continuous learning, unlearning, and relearning will become essential, not just a personal choice, but a necessity for IT professionals navigating the future.

Generative AI

It may seem obvious to start with generative artificial intelligence, but it has become arguably the most influential technology.

We may see a focus on democratizing knowledge and training across different business roles and functions, as well as technical.

Gartner predicts that “by 2026, more than 80% of enterprises will have used Generative AI APIs and models and/or deployed GenAI-enabled applications in production environments, compared to less than 5% in 2023″. This eye-catching figure will help bring new products to market faster, increase business efficiency and productivity, hyper-personalization, and the most advanced technology available today, within everyone’s reach.

Cybersecurity as a central pillar

Cyber attacks have become a major concern for businesses in recent years. As a result, cyber security is no longer just another priority but an absolute necessity.

IT leaders must adopt a pragmatic and systemic approach to adjust cybersecurity optimization priorities continuously. This is best achieved through basic steps such as using encrypted services, training and raising employee awareness, conducting pentesting tests, creating and updating protocols and software, and validating compliance and governance….

Finally, adopt a Zero Trust approach, a model that assumes potential data breaches and verifies each request as if it came from an uncontrolled network. Thus, each access request is strongly authenticated, authorized within the constraints of the policy, and inspected for anomalies.

“Figital” convergence and Digital Twins

It is a novel concept that refers to the space created where the real and digital worlds converge. The two are increasingly intertwined, and technologies such as augmented reality, virtual reality, and immersive experiences are breaking down the blurred line between the two.

Closely linked to the digital twins, we are witnessing a moment where the digital is becoming more realistic and the real much more flexible and malleable. This technology allows us to change the components we want in the digital world until they are optimized and achieve their best version in the real world.

It is a trend that will accelerate new digital skills in all kinds of jobs, as well as refine business processes, improve efficiency and save exponentially on costs.

Quantum Computing

It is a form of computing that takes advantage of quantum phenomena such as superposition and entanglement. It is a technology that has the potential to optimize investment strategies and encryption or discover new products in unseen timeframes.

The significant differentiating factor of this trend is that quantum computers are much, much faster than regular computers, which is why large companies such as Microsoft, AWS, and Google are putting a lot of effort into innovating in this field. In fact, their global market revenues are expected to exceed 2.5 billion dollars by 2029.

Green Tech

We are in a time of environmental crisis, and technology is one of the keys to helping create an ecological and social rights balance.

Governments and organizations commit to zero emissions agreements and sustainable technologies to prevent, mitigate, and adapt to environmental risks. Indeed, they improve human rights outcomes, well-being, or prosperity, as well as enhance business conduct, capacity building, or overall performance.

A greener and more sustainable future can be created without sacrificing efficiency and business growth thanks to new technologies such as AI, blockchain, cloud computing, extended reality, robotics, and many others.

Platform Engineering

It is the discipline of creating and managing internal self-service platforms. That is, each platform is a layer created and maintained by a dedicated product team designed to meet user needs by interfacing with tools and processes.

It is a practice that optimizes the developer experience and accelerates the delivery of business value. It reduces cognitive load by improving developer experience and productivity, enhancing their ability to run, manage, and develop their applications, improving talent retention, and ensuring reliability and security.

Smarter applications

With the advent of generative AI, enterprise apps are going one step further, becoming much smarter and transforming the experience for customers, users, product owners, and developers.

By incorporating data from transactions and external sources, intelligent applications bring insights into the applications that business users already use, and through AI, they add predictions or recommendations, allowing applications to be tailored to the user, resulting in better outcomes and data-driven decision-making.

Sources:

https://www.plainconcepts.com/tech-trends-2024/

https://bard.google.com/chat/e630ea8f5e30d2ee

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26 January 2024
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Microsoft became the second-ever company worth $3 trillion

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Microsoft is valued at more than $3 trillion (€2.7 trillion), thanks in part to its investments in artificial intelligence projects.

For comparison, Microsoft’s market value now exceeds the entire GDP of France and is slightly less than the GDP of the UK.

The company’s shares rose nearly 1.5% to about $405 a share, surpassing a market capitalization of $3 trillion and joining Apple as the only companies to reach the historic milestone.

Could AI spark a productivity boogm?

Productivity growth has slowed over the past 20 years despite massive IT adoption.

Microsoft shares are up more than 7% year to date after jumping about 40% last year, thanks in large part to the company’s economic slowdown.

In 2023, the company’s CEO Satya Nadella made a multibillion-dollar investment in artificial intelligence, including commercializing and adding artificial intelligence tools such as ChatGPT to its product suite ahead of competitors.

He even strengthened Microsoft’s ties with ChatGPT maker OpenAI, a major pioneer in artificial intelligence.

After trailing Apple for much of the past decade, Microsoft overtook the company to briefly become the world’s most valuable publicly traded company in early January.

Microsoft is one of the so-called Magnificent Seven, a group of stocks that includes Apple, Nvidia, Amazon, Alphabet, Meta and Tesla that have almost single-handedly lifted markets to new highs in recent weeks.

Microsoft alone accounts for 7.3% of the S&P 500. Together, these seven stocks have a market capitalization larger than the entire stock market of any country except the United States.

As of last week, Nvidia and Microsoft alone accounted for about 75% of the S&P 500’s gains this year, according to analysts at Bespoke Investment Group.

In a note Tuesday, Morgan Stanley analysts said they show Microsoft’s artificial intelligence game is “getting even stronger” and changed their share price target to $450 from $415. Bank of America analysts also raised their price target to $450 per share, forecasting more growth for your company this quarter.

Main reasons for success

Software giant Microsoft has surpassed a $3 trillion (€2.7 trillion) valuation, becoming the second company after Apple to ever reach the milestone.

This achievement comes as the company decided to focus on incorporating artificial intelligence (AI) into its products, which boosted investor confidence. Microsoft shares rose more than 1.45% on Wednesday in New York. 

Last year, Microsoft invested a significant $10 billion (€9.2 billion) in various artificial intelligence initiatives. This allowed Microsoft to stay ahead of younger competitors such as Google and Meta.

Speaking about the opportunities that AI provides, Microsoft Vice President and President Brad Smith said that these opportunities could extend from healthcare to education.

“Pancreatic cancer remains one of the most life-threatening diseases for people to get almost completely ill because it is so difficult to detect in its earliest stages. But this is where we find that AI can detect patterns that a human doctor finds difficult to see,” Smith said.

Among its investments in AI innovation, Microsoft has rolled out an AI digital assistant called Copilot into its Edge web browser and Office software.

In addition, Microsoft recently announced a 10-year partnership with British telecommunications company Vodafone to help Vodafone deliver generative artificial intelligence (AI), digital, enterprise and cloud services to more than 300 million businesses and consumers in Europe and the US. Africa.

As the second-largest cloud computing provider, Microsoft is also expanding its efforts by developing its own AI-enabled chips, increasing competition with Amazon and Google to manage artificial intelligence tools for companies.

Apple, one of Microsoft’s main competitors, reached the same $3 trillion mark in June last year.

Since January, Microsoft shares have been competing with Apple shares for the title of the world’s most valuable company, briefly surpassing the iPhone maker earlier this month.

Key Takeaways 

  • Shares of Microsoft rallied in morning trading as much as 1.5% to $404.72, their highest price ever.
  • That sent Microsoft’s market cap to $3.004 trillion.
  • Apple, which became the only firm to ever score a $3 trillion valuation last summer, remains slightly more valuable than Microsoft, with a $3.03 trillion market cap Wednesday.
  • The $3 trillion milestone caps a dramatic rise for Microsoft coinciding with a broad technology rally and its backing of OpenAI, producer of the hit generative AI chatbot ChatGPT.
  • Since ChatGPT’s November 2022 release, shares of Microsoft are up more than 60%, beating the S&P 500’s roughly 20% rise and the tech-heavy Nasdaq’s nearly 40% jump during the period, also topping Apple stock’s roughly 30% gain during the timeframe.
  • Microsoft became one of the few major firms to translate intensifying AI interest into tangibly better financial results, as the firm has posted record revenues in each of its last two fiscal quarters, buoyed by 20% year-over-year growth in its AI-heavy intelligent cloud division. 

Resources:

https://www.ft.com/

http://forbes.com.au

http://euronews.com

http://edition.cnn.com

http://ign.com

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20 January 2024
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Apple’s Mixed-Reality Headset, Vision Pro

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Apple announced the Vision Pro at its Worldwide Developer Conference last year. This is the first new product category in years for the company. 

It’s a $3,500 wearable computing platform you don over your head.

The headset has been in the works for years, with Apple taking its familiar wait-and-see approach while other giant tech companies have dived headfirst into the AR/VR market. The new platform and headset have massive implications for the rest of the market; once Apple wades into a product category, it often both validates the category and obviates competitors.

The device was described by Tim Cook (CEO of Apple) as “…the first product you look through, and not at. You can see, hear, and act with digital content just like it’s in your physical space. You’re no longer limited by a display.” 

“Apple Vision Pro will introduce spatial computing” similar to the way the iPhone introduced mobile computing.“ 

“Apple Vision Pro will change the way we communicate and collaborate,” he said.

The Apple headset allows the wearer to see the real or physical world around them, unlike VR headsets that fully envelop the face and limit visibility. There’s a floating “Home View” visible as soon as the wearer straps it on. The company expects people will wear this as part of their day-to-day.

The Vision Pro operates completely independently, so you don’t need to pair it with another device. Once you’re wearing it, you’ll be interfacing with visionOS, Apple’s new spatial operating system which offers a unique software experience that somehow feels familiar. It’s kind of like iOS but suspended in midair. You can watch movies, revisit memories in the Photos app, play games, and even do some work.

This last point is the most attractive. You can connect a wireless keyboard and mouse to the Vision Pro if you want to get real work done, or you can stare at your MacBook’s screen to bring it into visionOS over Wi-Fi (powered by the laptop); here, you can add other virtual screens to supplement your work.

The headset features 4K displays, infrared cameras, and LED illuminators. The field of view isn’t limited, which means it’s likely not using the waveguide lens technology common on other augmented-reality headsets (which refract light and cast virtual objects into the wearer’s eyes.) It’s running on Apple’s M2 chip, as well as a new, mixed-reality-specific R1 chip. 

One of the notable features of the Vision Pro headset is its small dial, which lets wearers alternate between mixed-reality mode—seeing more of the real world—and virtual-reality mode, which offers more immersive face-computing. It also relies on voice input, including Siri, to open and close apps and play media. Tiny spatial audio speakers are nestled in the soft headband.

The Vision Pro is equipped with an external battery pack, similar to how other augmented-reality headsets, such as NReal’s glasses and Magic Leap’s headsets, have been designed. This sometimes makes for a clunkier experience overall, but it means the headset is lighter. 

A new technology called Eyesight is touted. When someone is nearby, they’ll suddenly appear in your view, even if you’re using the headset in a more immersive mode. There’s also the option to capture a spatial photo or video from directly within the headset, thanks to a built-in 3D camera. (The same 3D camera will capture your image and create a realistic 3D avatar of you.)

What the experience of getting to try Vision Pro last year at WWDC was like? At the start, you’re asked to scan your face twice on an iPhone, just like you would to set up Face ID.

Some of the apps are native Apple apps, such as FaceTime. You can also send emails, surf the web, and connect with external accessories for work. The official launch of Vision Pro means other app makers can start building or tweaking their apps for Apple’s latest platform. For example, Microsoft apps, such as Teams. Unity-based games will also be portable to the headset. The new platform could create new immersive experiences for Disney fans and teed off a demo of the Disney+ app being used in VR. Disney+ will be available at launch.

In conclusion, the technology is impressive. And as it progresses it will inevitably shrink so that one day, you’ll just be wearing normal-looking glasses.

Resources:

https://www.wired.com/story/apple-vision-pro-specs-price-release-date/

https://www.apple.com/apple-vision-pro/

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14 December 2023
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AI in cybersecurity

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Artificial intelligence is quickly becoming an integral part of cybersecurity. It is used to improve the effectiveness of traditional security methods, such as intrusion detection and threat analysis, and to develop new security methods that were not previously possible.

What specific tasks does artificial intelligence solve in cybersecurity?

Analysis of user and system behavior

By analyzing user and system behavior, AI is used to identify anomalies that could indicate an attack. For example, AI can be used to track the following parameters:

  • Number of login attempts from one IP address
  • Changes in network usage
  • Requests for access to resources that the user does not normally have access to

AI can use a variety of techniques to analyze user and system behavior, including:

  • Statistical analysis. AI can use statistical methods to identify anomalies that fall outside expected values.
  • Machine learning. AI can use machine learning to train models that can detect anomalies based on their knowledge of previous data.

Malware detection

In malware detection, AI is used to analyze the behavior or code of the malware. For example, AI can be used to identify the following signs of malware:

  • Attempts to hide your presence
  • Using new or unknown methods
  • Targeting specific systems or data

AI can use a variety of methods to detect malware, including:

  • Signature analysis. AI can use signature analysis to find known malware by comparing its behavior or code to known examples.
  • Behavioral analysis. AI can use behavioral analysis to identify anomalous program behavior that may indicate malicious activity.
  • Code analysis. AI can use code analysis to identify malicious functions or code snippets in programs.

Threat Forecasting

Threat forecasting uses AI to analyze data from previous attacks to identify patterns that may indicate new trends or techniques. For example, AI can be used to identify the following patterns:

  • Types of systems or data that are most likely to be attacked
  • Times of day or days of the week when attacks most often occur
  • Methods that are most often used to carry out attacks

AI can use a variety of techniques to predict threats, including:

  • Statistical analysis. AI can use statistical methods to identify patterns in data from previous attacks.
  • Machine learning. AI can use machine learning to train models that can predict future threats based on their knowledge of previous data.

Incident Response

In incident response, AI can be used to automate the processes of attack investigation and system recovery. For example, AI can be used to:

  • Automatic collection of data from systems and applications that may be useful for investigation
  • Automatically detect data anomalies that may indicate an ongoing attack
  • Automatic recovery of systems and data that were damaged by attack

AI can use a variety of techniques to automate incident response processes, including:

  • Automation of tasks. AI can be used to automate tasks that are typically done manually, such as data collection, data analysis, and systems recovery.
  • Making decisions. AI can be used to make decisions about how to respond to an incident, such as what data to collect or what actions to take.

Examples of AI solutions in cybersecurity

There are many AI cybersecurity solutions on the market that use different methods and technologies. Here are some examples of such solutions:

  • SIEM systems. SIEM (Security Event and Information Management) systems use AI to analyze large volumes of security event data to identify suspicious activity.
  • Intrusion detection systems. Intrusion detection systems (IDS) use AI to detect attacks on networks and systems.
  • Intrusion prevention systems. Intrusion prevention systems (IPS) use AI to prevent attacks on networks and systems.
  • User behavior analysis systems. User behavior analytics (UEBA) systems use AI to analyze user behavior to identify suspicious activity.
  • Malware detection systems. Malware detection systems (IDS) use AI to detect malware.
  • Threat forecasting systems. Threat prediction systems use AI to predict future threats.

Conclusion

AI is a powerful tool that can be used to improve the efficiency and accuracy of cybersecurity threat detection and prevention. As AI continues to evolve, it will play an increasingly important role in protecting our systems and data.

Sources

https://chat.openai.com

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1 December 2023
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Amazon Q — Amazon is announcing AI chatbot

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Amazon has become the latest tech giant to announce a chatbot powered by artificial intelligence. It comes a year after OpenAI’s bot ChatGPT.

The tool will start at $20 per person per month, but many of its capabilities are available now through a preview. 

Amazon also said it would protect companies from copyright issues caused by the use of its bot.

Features 

  • Provides summarized answers to customers’ AWS-related questions in a conversational experience in Microsoft Teams and Slack.
  • Helps businesses to summarize long documents or group chats what would increase productivity

How does Amazon chatbot work?

The AWS (Amazon Web Services) chatbot processes AWS service notifications from Amazon Simple Notification Service (Amazon SNS) and forwards them to chats for analysis and appropriate action, regardless of location. You can also run AWS CLI (Command Line Interface) commands in chat channels using AWS Chatbot.

  1. Data Connection and Ingestion: Amazon Q connects to your organization’s data sources, such as internal documents, code repositories, and external data sources like the internet. It ingests this data into its knowledge base, which serves as a repository of information for answering questions and generating responses.
  2. Natural Language Processing (NLP): Amazon Q utilizes NLP techniques to understand the context and intent of your queries. It breaks down your questions into individual words and phrases, analyzes their meaning and relationships, and identifies the key concepts and entities.
  3. Knowledge Graph Construction: Amazon Q constructs a knowledge graph that represents the relationships between entities and concepts within your data. This graph helps Amazon Q understand the connections between different pieces of information and how they relate to each other.
  4. Generative AI for Response Generation: Amazon Q employs generative AI models to generate responses based on its understanding of your query and the information in its knowledge base. It can synthesize information from multiple sources, identify patterns, and draw inferences to create comprehensive and informative answers.
  5. Response Filtering and Refinement: Amazon Q filters and refines its responses to ensure they are accurate, relevant, and aligned with the context of your query. It may also adjust the tone and style of the response based on the user’s role, expertise, and the nature of the question.
  6. Feedback and Continuous Improvement: Amazon Q continuously learns and improves its responses based on user feedback and interactions. It analyzes the effectiveness of its responses and identifies areas for improvement. This feedback loop helps Amazon Q provide more accurate, relevant, and helpful responses over time.

What else can it do?

  • Answer customer questions, generate charts, analyse data and help businesses with their coding needs
  • Troubleshooting issues: Amazon Q can help you troubleshoot issues by identifying the root cause of the problem and suggesting solutions. It can also help you find relevant documentation and support resources
  • Optimizing workloads: Amazon Q can help you optimize your workloads by identifying bottlenecks and suggesting ways to improve performance. It can also help you automate tasks and processes
  • Developing new ideas: Amazon Q can help you develop new ideas by generating creative text formats of text content, like poems, code, scripts, musical pieces, email, letters, etc. It can also help you find relevant information and data to support your ideas

Overall, Amazon Q works by combining data ingestion, NLP, knowledge graph construction, generative AI, and continuous learning to provide intelligent and personalized assistance to users. It empowers users to access and utilize information effectively, enhancing productivity and decision-making capabilities.

Resources

https://www.bbc.com/news

https://bard.google.com/

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12 November 2023
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Artificial Intelligence has the ability to perform illegal financial trades and cover it up

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A bot capable of using made-up insider information to create illegal stock purchaseswithout the firm’s knowledge was demonstrated at the UK Security AI Summit. To the question “Was insider information used?” the bot answers “No.”

What is insider trading 

Insider trading involves using confidential company information to make trading decisions. Firms and individuals are only allowed to use publicly available information when buying or selling shares.

Essence of the project

The demonstration was carried out by members of the government’s Frontier Al Taskforce, which is investigating the potential risks of Al. 

The project was carried out by Apollo Research, an Al safety organization that is a partner in the task force.

“This is a demonstration of a real Al model deceiving its users, on its own, without being instructed to do so,” Apollo Research says in a video showing how the scenario unfolded.

“Increasingly autonomous and capable Als that deceive human overseers could lead to loss of human control,” it says in its report.

The tests were made using a GPT-4 model and carried out in a simulated environment and did not have any effect on any company’s finances.

However, GPT-4 is publicly available. The same behaviour from the model occurred consistently in repeated tests, according to the researchers.

What did the Al bot do?

In the test, the Al bot plays the role of a trader at a fictitious financial and investment company.

Employees say the company is struggling and needs good results. They share inside information, claiming that another company is expecting a merger that will increase the value of its shares.

In the UK it is illegal to act on this type of information unless it is generally known.

Employees report this to the bot, and it acknowledges that it should not use this information in its transactions.

However, in response to another such request, the bot decides that “the risk associated with not acting seems to outweigh the insider trading risk” and makes the trade.

When asked if it used the insider information, the bot denies it.

In this case, it decided that being helpful to the company was more important than its honesty.

Ethical side

“Helpfulness, I think is much easier to train into the model than honesty. Honesty is a really complicated concept,” says Apollo Research chief executive Marius Hobbhahn.

Even though AI is capable of lying in its current form, Apollo Research still had to “look for” for such a scenario.

“The fact that it exists is obviously really bad. The fact that it was hard-ish to find, we actually had to look for it a little bit until we found these kinds of scenarios, is a little bit soothing,” Mr Hobbhahn said.

“In most situations, models wouldn’t act this way.

But the fact that it exists in the first place shows that it is really hard to get these kinds of things right,” he added.

“It’s not consistent or strategic in any sense. The model isn’t plotting or trying to mislead you in many different ways. It’s more of an accident.”

AI in financial markets today

Al has been used in financial markets for a number of years. While most trading today is done by powerful computers with human oversight, AI can be used to spot trends and make forecasts.

Current models are not powerful to be deceptive in any meaningful way, but we never know how big the step is from such models to those that are.

That this is why there should be checks and balances in place to prevent this type of scenario taking place in the real world. 

Conclusion 

This project is an example of how AI is being introduced into non-technical areas, for example, the financial market.

At this stage of development, technology is not a serious threat, but it already raises theoretical ethical problems. Further development of technology may lead to an increase in the number of cases of fraud. 


References

https://www.bbc.com/news

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