Multimodal AI Systems
Multimodal AI systems are evolving to handle multiple forms of input and output, enhancing their versatility and functionality. They are revolutionizing various domains, exemplified by their integration into popular technologies such as virtual assistants, self-driving cars, augmented reality (AR), social media content moderation, and medical diagnosis tools.
Virtual assistants like Amazon's Alexa or Apple's Siri can understand and respond to spoken language and process visual information through camera input, enabling tasks like displaying images on screens, playing music, or controlling smart house devices.
Self-driving cars utilize multimodal AI to fuse data from sensors like lidar, radar, and cameras to navigate surroundings and track other vehicles and pedestrians.
In AR applications, multimodal AI facilitates the overlay of digital content onto the real world, offering features like repair instructions or city exploration guidance.
Moreover, social media platforms leverage multimodal AI to automatically detect and remove harmful content, while medical professionals use it to analyze medical images and patient data to aid diagnosis.
Other examples:
- Google AI Assistant: With the ability to recognize songs from humming, identify animal sounds, and perform various tasks based on voice commands, Google AI Assistant exemplifies multimodal learning.
- OpenAI's CLIP: Contrastive Language-Image Pre-training (CLIP) is a model proficient in classifying images and generating text descriptions, demonstrating the integration of text and image modalities.
- OpenAI's DALL-E: Specializing in generating images from text descriptions, DALL-E showcases the synthesis of text and image data to create novel visual content.
- GitHub Copilot: A code-writing AI assistant, GitHub Copilot illustrates how AI can understand and generate code based on textual inputs, facilitating software development tasks.
These examples underscore multimodal AI systems' versatility and transformative potential across diverse industries.
Generative AI Advancements
Generative AI, a cutting-edge field of artificial intelligence, is transforming numerous industries by producing entirely novel content. Gartner's Trends report predicts that generative AI adoption will increase dramatically by 2026, with more than 80% of enterprises integrating generative AI APIs, applications, and models into their operations, compared to less than 5% today.
For instance, take image generation, where tools like DALL-E 2 or MidJourney can craft realistic and imaginative images based on textual descriptions, allowing for quick visualization of concepts like a "cake flying across the ocean." Moreover, generative AI extends its creative prowess to music composition, text generation, and even code writing, enabling tasks like generating a "cat jazz tune for studying" or assisting in programming tasks. Furthermore, this technology is leveraged to generate short videos from descriptions, presenting opportunities for social media content creation and educational content development. By 2024, there have been significant developments in generative AI models that have expanded creativity and contextual understanding across various media formats. Innovations such as Gemini and ChatGPT have showcased the evolution of multimodal AI systems. These systems integrate diverse models to enhance functionality across different inputs and outputs.
Furthermore, startups specializing in GenAI are attracting more and more large investors.
Ethical and Responsible AI
Światowa Organizacja Zdrowia wyróżnia 3 sygnały wypalenia zawodowego, na które powinieneś zwrócić uwagę:
1. Ekstremalny Cynizm
Masz wrażenie, że Twoje negatywne odczucia związane z pracą wypierają te pozytywne? Czujesz, że Twoja praca nie jest doceniana, jest beznadziejnie i nie da się nic z tym zrobić? Cynizm jest w rzeczywistości czerwoną flagą wypalenia zawodowego. Jeśli postrzegasz (lub ktoś z Twojego zespołu postrzega) pracę jako pozbawioną większego sensu. Najwyższy czas zastanowić się, czy to nie oznaka większego problemu.
Czym przejawia się cynizm?
- Utratą sensu.
- Ciągłą frustracją.
- Poczuciem rozczarowania.
- Brakiem radości z wykonywanej pracy.
To może mieć wpływ na Twoją wydajność w pracy i wydajność innych osób w zespole. Przykład: jeśli pracujesz w dziale wsparcia IT, możesz odpowiedzieć na ticket linkiem do Let Me Google That For You. I włożyć minimalny wysiłek w pomoc, bo przecież nic się nie liczy. W rezultacie pozostałe osoby z Twojego teamu mogą nie chcieć poprosić Cię o pomoc, bo wiedzą, jakiej odpowiedzi mogą się spodziewać.
2. Przewlekłe wyczerpanie
Czujesz, że ciągle brakuje Ci energii, a Twoja praca nie jest tak efektywna? Masz problem z zebraniem się do pracy. Nie jesteś w stanie wrzucić najwyższego biegu i czujesz się wyczerpany pod koniec dnia? Każdy z nas może mieć gorszy dzień. To zupełnie normalne. Jednak w tym przypadku odpoczynek i krótka przerwa nie zdają egzaminu. Osoby z przewlekłym wyczerpaniem czują się tak, jakby cała energia wyparowała i (co gorsza) nie mogą jej odzyskać.
Oto kilka oznak wyczerpania na poziomie wypalenia:
- Brak energii.
- Zerowa motywacja.
- Niezdolność do koncentracji.
- Poczucie ogromnej ulgi na weekend.
- Strach przed wzięciem wolnego, aby odpocząć.
3. Negatywna samoocena
Branża IT charakteryzuje się wysokimi wynikami, dlatego poczucie, że odstajesz od reszty zespołu i nie dajesz sobie rady, może być szczególnie niszczące. Przecież nikt z nas nie lubi czuć się niekompetentny. Nie chcemy też być kulą u nogi dla reszty naszych współpracowników. Czuć, że stoimy w miejscu. Jeśli masz wrażenie, że pracujesz całe dnie, ale nic nie osiągasz lub wszystko, co robisz, jest złe. Może to być oznaką wypalenia zawodowego.
Jak mają się do tego badania?
Statystyki są przerażające i mówią same za siebie. Badanie „The State of Burnout in Tech 2022” przeprowadzone na ponad 30.000 specjalistach IT wykazało, że:
- 51% czuje, że osiąga mniej, niż powinno.
- 56% mężczyzn i 69% kobiet nie potrafi się zrelaksować po zakończeniu dnia pracy.
- 43% czuje się niezaangażowanych w swoją pracę, a 27% nie widzi w niej żadnej wartości.
Ethical and regulatory concerns have become more prominent with the advancement of artificial intelligence (AI). The responsible use of AI has become a significant trend, focusing on transparency and ethics to prevent social harm. This movement addresses issues such as data privacy, bias, and explainability and sets standards for the ethical development of AI. In the year 2024, there will be more emphasis on developing AI systems that are transparent, explainable, and free of bias. The regulatory framework is also being updated to address the ethical challenges of AI, specifically in surveillance, data privacy, and autonomy.Summary of the World's First AI LawOn March 13, 2024, the European Parliament approved a new regulation. The Artificial Intelligence Act is the first law to regulate the AI sector comprehensively. The new regulations make it possible to build a legal environment that, on the one hand, will provide opportunities for the development of artificial intelligence and, on the other hand, guarantee respect for human rights and minimize discrimination. Every AI system should adhere to three essential conditions: be lawful, comply with ethical principles for trustworthy artificial intelligence, and ensure safety and technical resilience.Therefore, new regulations have been introduced to prohibit certain practices in artificial intelligence. These include the use of subliminal techniques or any form of discrimination against certain groups of individuals. Additionally, AI systems cannot be used to evaluate civilians or track their lifestyle; thus, social scoring is prohibited. There are safeguards on general-purpose artificial intelligence and limitations on using biometric identification systems by law enforcement. Furthermore, the use of AI to manipulate or exploit users is banned. Consumers have the right to launch complaints and receive meaningful explanations.The AI Act establishes clear requirements and obligations for developers and deployers of AI technology. It aims to regulate the use of AI in specific areas while simultaneously reducing the administrative and financial burden on businesses, especially small and medium-sized enterprises (SMEs).Non-compliance with the AI Act may result in fines of up to 7% of global turnover, with the primary liability falling on AI system providers. The European AI Office will be responsible for enforcing the regulations. The Council's formal endorsement is still pending. The AI Act is expected to be implemented gradually over a transitional period and will become fully applicable within 24 to 36 months.AI TrismAI TRiSM ( Trust, Risk, and Security Management) is a framework designed to ensure the ethical deployment of AI technologies. It covers various aspects such as explainability, ModelOps, data anomaly detection, adversarial attack resistance, and data protection. The framework is crucial in enabling responsible AI deployment, becoming increasingly important as organizations adopt more AI. According to Gartner's insights, by 2026, companies that use AI TRiSM to manage their AI systems are expected to make better decisions by removing up to 80% of inaccurate or fake data.
Realty Check for AI - more realistic expectations
Artificial intelligence (AI) holds immense promise, yet tempering our enthusiasm with realism is crucial. it's essential to recognize that AI builds upon existing technologies like statistics, which have long been used to solve business problems. Even the most sophisticated AI algorithms require human oversight and well-defined goals to deliver tangible value. Moreover, history teaches us that technology integration often takes longer than anticipated. While AI presents exciting possibilities, hurdles such as cultural resistance, data collection challenges, and regulatory complexities may impede its widespread adoption. Additionally, technology's unintended consequences underscore the need for cautious optimism. While AI has the potential to boost productivity and revolutionize industries, it may also introduce new challenges, from cybersecurity risks to distractions in the form of entertainment options.
computing facility or private data center." Instead of sending data to distant servers, it is processed directly on the edge devices where data is collected, whether in a factory, hospital or even in our pocket on our phones. This enables data to be processed within milliseconds, allowing immediate real-time feedback.
Recent innovations in neural networks, advances in computing infrastructure, and the widespread adoption of IoT devices have enabled the deployment of AI models at the edge. What does this mean for users? Edge AI offers multiple benefits, such as powerful and flexible intelligence, real-time insights, reduced cost, increased privacy, high availability, and persistent improvement of AI models. Imagine factories predicting when a machine might break down before it even happens, doctors receiving real-time assistance during surgery, or you ordering groceries simply by talking to the refrigerator. This is how Edge AI works.
What’s the difference between edge AI and cloud AI?
Computing power: Cloud AI boasts superior computational capabilities and storage capacity, enabling complex model training and deployment. In contrast, edge AI is limited by device size, constraining processing capacity.
Latency: Edge AI minimizes latency by processing data on-device, enhancing productivity and user experience. Cloud AI, however, relies on distant servers, leading to higher latency and slower response times.
Network bandwidth: Edge AI requires lower bandwidth as data processing occurs locally, while cloud AI necessitates higher bandwidth for data transmission to remote servers.
Security: Edge AI prioritizes privacy by handling sensitive data locally, reducing exposure to external servers. Conversely, cloud AI involves data transmission to external servers, potentially compromising data security.
Smaller AI models - how much electricity is consumed by AI models?
Understanding the energy consumption of AI models is a complex challenge. While estimates exist, the actual cost remains elusive, as organizations like Meta, Microsoft, and OpenAI are not transparent about their energy usage. Training AI models, huge ones like GPT-3, is exceptionally energy-intensive, consuming as much electricity as 130 US homes annually. However, deploying these models for user interaction (inference) consumes less energy. For instance, tasks like classifying text samples or generating text require minimal energy compared to tasks involving image generation. Despite efforts to estimate energy usage, the variability in AI models and the need for standardized data make it challenging to determine precise figures. Experts emphasize the need for transparency and suggest measures such as energy star ratings for AI models to promote energy efficiency and inform consumers. As the AI sector continues to grow, understanding and addressing its energy footprint will be crucial for sustainable development and mitigating environmental impact.
Open source AI
The rise of open-source AI marks a significant shift in the landscape of artificial intelligence, offering developers and researchers unprecedented access to collaborative environments and cutting-edge technologies. With 80% of respondents in the 2023 State of Open Source report noting a surge in open-source software usage and 41% citing a substantial increase, it's clear that this movement is gaining traction. Open-source AI involves freely accessible source code, fostering collaboration among enthusiasts to expedite the development of practical solutions. Platforms like GitHub host projects pivotal in digital innovation across sectors, empowering developers to address complex challenges efficiently. Examples of open-source AI models gaining popularity include GPT-J, TensorFlow, LLaVa, Adept, and Qwen-VL, among many others, which offer transparency, flexibility, and cost-effectiveness. With free access to cutting-edge technologies and the acceleration of impactful applications' development, the future of AI lies increasingly in open-source solutions. Forrester reports that 85% of enterprises are incorporating open-source AI models into their tech stacks, highlighting the momentum behind this movement.
Customer service - AI virtual assistance
The State of AI in Customer Service: 2023 Report by Intercom sheds light on the evolving landscape of AI trends in customer service, mainly focusing on AI virtual agents.
Companies increasingly invest in AI for customer service, with 69% of support leaders planning to ramp up their investments in the coming year. While AI enhances efficiency and saves costs, it's expected to complement, not replace, human agents. Over three-quarters of customer service leaders anticipate AI transforming customer support roles positively within five years. Integrating AI and automation streamlines operations and provides a competitive advantage in delivering exceptional customer experiences. However, there's a discrepancy between support leaders' optimism and the readiness of consumers to interact with AI chatbots, as highlighted by Gartner's research. This misalignment raises questions about the immediate prospects of AI in customer service.
Conclusion
In 2024, the latest trends in AI are revolutionizing various industries. Multimodal AI systems can handle multiple inputs, including virtual assistants and self-driving cars, while generative AI is used to create fresh content. It is important to balance innovation with ethical considerations and adhere to EU regulations to ensure responsible deployment. These trends present opportunities and challenges for the future of artificial intelligence, which is transforming industries. Multimodal AI systems handle a variety of inputs, such as virtual assistants and autonomous cars, while generative AI creates new content. Balancing innovation with ethical artificial intelligence and EU regulations ensures responsible deployment. These trends offer enormous opportunities for the future of artificial intelligence.
Sources:
https://www.miquido.com/blog/gen-ai-and-ai-difference/
https://datamatters.sidley.com/2024/03/21/eu-formally-adopts-worlds-first-ai-law/
https://www.linkedin.com/pulse/new-2024-state-edge-ai-report-wevolver-xcyte/
https://www.gartner.com/en/articles/gartner-top-10-strategic-technology-trends-for-2024
https://www.theverge.com/24066646/ai-electricity-energy-watts-generative-consumption
https://www.intercom.com/campaign/state-of-ai-in-customer-service
https://www.ibm.com/topics/edge-ai
https://blogs.nvidia.com/blog/what-is-edge-ai/
