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Nexxt Intelligence

“The collective progress in this field showcases Active Inference’s potential as a systematic blueprint for Artificial General Intelligence (AGI).” As Chief Scientist at and pioneer of the Free Energy Principle that underlies Active Inference, Friston and his VERSES team have developed an entirely new kind of that mimics biological intelligence, able to take any amount of data and make it ‘smart.’ VERSES AI AI In an exclusive presentation at the recent held in Ghent, Belgium, this past September 13–15, 2023, world-renowned neuroscientist Dr. Karl Friston gave fewer than 100 global Active Inference experts and researchers a glimpse into the future of next-generation artificial intelligence. His talk provided perspective into some of the latest research related to state spaces, universal generative models, belief sharing between agents, active learning, and structured learning. 4th International Workshop on Active Inference (IWAI) Dr. Friston is at the forefront of developing ‘Active Inference’ models, revolutionizing how intelligent systems learn and adapt. The information shared at IWAI is built on more than a decade of groundbreaking work and collaboration with fellow researchers in the at University College London. Nested Minds Network Watch the full episode NOW on or . YouTube Spotify The Fabric of Intelligence: State Spaces and Factor Graphs At the heart of understanding intelligence lies the idea of ‘generative world models’ — these are probabilistic frameworks that encapsulate the underlying structure and dynamics involved in the creation of sensory data. As Friston explained, developing a that can be widely applied has been a core pursuit. universal generative world model The challenge lies in modeling the intricacies of ‘state spaces’ — capturing not just sensory data itself but the hidden variables and control factors that determine how stimuli unfold over time. By incorporating hierarchical and factorial structure, increased temporal depth, and an emphasis on Agent dynamics and control paths, Friston has worked to construct expressive yet interpretable models of complex environments. As he suggested, the resulting grammar of cognitive machinery — factor graphs equipped with message passing — may provide a foundation for advancing distributed and embedded intelligence systems alike. Exploring the Nature of State Spaces A significant portion of the talk focused on state spaces within these generative models. Friston highlighted how understanding these spaces is vital for grasping the dynamics of how our brain predicts and reacts to stimuli. Friston has focused heavily on developing more complete generative models of disputed state spaces, generalizing and expanding on partially observed Markov decision processes. His models place special emphasis on representing paths and control variables over time, while accounting for a variety of factors that can operate independently or in conjunction with each other, across different layers or levels of the model. The direct modeling of dynamics and control in his work sheds new light on understanding concepts like intentional planning as opposed to behaviors influenced by prior preferences. hierarchical framework through various time scales, Emergent Communication Systems Among Multiple Agents . Friston showed that when Intelligent Agents pursue ‘active learning’ with a focus on gaining information, this approach inherently leads to the development of common communication methods and linguistic structures. Essentially, as Agents strive to enhance their model’s accuracy and effectiveness by exchanging beliefs, self-organized patterns evolve, spontaneously establishing a basis for mutual understanding and predictability. Intelligence also relies on interacting with other Agents Developing methods for seamlessly pooling knowledge and achieving consensus while retaining specialization is an active research direction. The integrated components must balance stability with flexibility, adapting core beliefs slowly while remaining receptive to external influences. As Friston noted, much like human maturational trajectories, childlike malleability fades, yet the integration of new ideas remains possible through niche environments. Assembling a Universal Generative World Model A universal ‘world model’ that can be widely applied remains an ambitious goal. Friston discussed two potential approaches — an initially simple model that grows structurally to capture more complexity versus a massively interconnected model that relies more on attentional spotlights. He also noted the potential for composing such models from subgraphs of specialized Intelligent Agents, allowing for parallel development of appropriately-scoped pieces. Belief sharing through communication protocols, such as the developed by VERSES AI, enables the learning Agents to converge on a mutually predictable, and thereby more efficient, joint world model. Spatial Web Protocol Belief Sharing Through Active Learning Belief sharing and its impact on active learning formed another cornerstone of Dr. Friston’s presentation. He revealed how our brains share and update beliefs, a process central to learning and adapting to new environments. In his talk, Friston demonstrated belief sharing in an example with multiple Intelligent Agents playing a hiding game. This emergent communication system arises naturally from the drive to maximize the information and efficiency of the shared world model. By actively learning the mapping between beliefs and communicative utterances, the Agents can resolve uncertainty and synchronize their internal models via the utterances that propagate beliefs. Information-Theoretic Insights into Active Learning In a more technical sense, Friston views active learning as a process where updates to the model are seen as actions aimed at reducing expected free energy. This perspective is rooted in information theory, with free energy representing the gain in information, bounded by certain preferences that arise from updating specific parameters. As a result, the model learns mappings that are both sparse and rich in mutual information, effectively representing structured relationships. Friston illustrated this through a simulation of Intelligent Agents jointly self-organizing around a shared language model. Structured Learning: A New Perspective Finally, structured learning was discussed as a novel approach within the realm of . Dr. Friston provided insights into how structured learning frameworks could offer more efficient ways of understanding and interacting with the world. active inference Structured Learning through Guided Model Expansion Friston explored active model selection, framing the expansion of a model’s structure as similar to goal-directed optimization through expected free energy. Gradually introducing new elements, such as states or factors, following simple heuristic principles, allows for the automatic expansion of the model’s complexity, to better reflect and improve its alignment with the learning environment. Friston demonstrated applications ranging from unsupervised clustering to the development of distinct, non-overlapping representations in an Agent simulation. Structured Deep Learning Intelligence requires not just statistical modeling, like what we see in current AI machine learning/deep learning systems, but recognizing structural composition — the patterns of parts to wholes and dependencies that characterize natural systems that occur in the real world. Friston outlined strategies for progressively evolving the architecture of real-world models and making improvements to modular elements in response to fresh data, all centered around clear goals. The guided increase in complexity astonishingly leads to the natural development of organized and distinct feature areas, classifies them into clear categories, and controls their behavior patterns, all from raw multimedia data streams. Such capabilities get closer to flexible human-like learning that moves far beyond the current passive pattern recognition systems of today, into Autonomous Intelligent Systems interacting with and reshaping their environments. The Future of Active Inference AI Overall, Dr. Karl Friston’s presentation at IWAI 2023 demonstrates that his work continues to push Active Inference AI into new territory through innovations like inductive planning, information-theoretic formalisms, emergent communication systems, and structured learning. As the models, methods, and insights mature, the prospects grow ever stronger for realizing artificial intelligence as multitudes of Intelligent Agents that can learn, act, and cooperate at the level of human intelligence. The collective progress in this field showcases Active Inference’s potential as a systematic blueprint for Artificial General Intelligence (AGI). While Friston’s presentation was meant only as a glimpse, The foundation of Dr. Karl Friston’s impact in the realm of AI is driven by his clarity of vision and deep intellectual insight that are actively shaping the evolution of this next era of these technologies. these insights keep VERSES AI at the leading edge of creating Autonomous Intelligent Systems (AIS) that can understand, communicate, and adapt as humans do. Watch NOW on : YouTube Episode Chapters: 00:00 — Introduction by Tim and Karl Friston’s Opening Remarks 06:09 — The Significance of Free Energy in Understanding Life 07:10 — Exploring Markov Blankets and Factor Graphs 09:56 — Hierarchical Generative Models and Time Perception 13:36 — Active Inference and Planning with Expected Free Energy 15:40 — Joint Free Energy and Generalized Synchrony in Physics 16:40 — The Role of Paths and Intentions in Physics and Active Inference 17:35 — Specifying Endpoints of Paths as Goals 30:26 — Communication and Belief Sharing in Federated Systems 31:35 — Generative Models and Perception in Agents 40:19 — Model Comparison and Latent State Discovery 41:25 — Active Model Selection and Handwritten Digit Styles 45:30 — Structured Learning and Object Localization 49:10 — Epistemic Foraging and Confidence in Learning 50:21 — The Dynamics of Information Gain and Learning 51:29 — Conclusion and Q&A Begins 01:19:33 — Emphasis on Constraints in the Free Energy Principle 01:23:40 — Closing Remarks 01:24:55 — Karl Friston’s Reflections on the Field’s Evolution and the Next Generation , and Explore the future of AI with Active Inference and gain insights into this revolutionary approach! Visit my blog at https://deniseholt.us/ Subscribe to my channels on , YouTube , Spotify , Apple , and more, and subscribe to my newsletter on Amazon , for more ground breaking content. LinkedIn Also published . here

Exclusive Interview: Dr. Karl Friston's Blueprint for Artificial General Intelligence

Imposter syndrome can feel paralyzing, especially for those in the technology industry. Tech professionals face mounting stresses to be successful at their jobs. Endless developments and a cut-throat market might make you wonder whether you belong. Combatting imposter syndrome is necessary to make a name for yourself in technology. The good news is you're you're not alone. Let's explore the prevalence of imposter syndrome in tech and how to find assurance in your abilities. The Ever-Changing Technology Industry The tech industry has evolved tremendously with applied artificial intelligence (AI), next-generation software development, quantum technologies, and advanced connectivity. Yet, while the field remains one of the fastest-growing, big-name tech giants like Microsoft, Amazon, and Meta in 2023. accounted for over 240,000 layoffs Job uncertainty and lagging aptitude in the latest innovations — less than half of 3.5 million job postings have available talent with the necessary qualifications — have many questioning their chance of succeeding. Nearly considered changing careers in 2023, with 44% ready to leave their jobs for something else. 50% of American workers From a different perspective, current industry developments present unique opportunities for you to climb the career ladder. All you need is initiative, a positive outlook, and a little faith in yourself. How Common Is Imposter Syndrome in Tech? In a 2020 study from the University of California, San Diego, frequently felt imposter syndrome — 71% were women compared to 52% of men. Those feelings often trickle into the professional world, too. 57% of computer science students Technology careers are highly demanding and competitive, leading many to believe in their own inadequacies. As workers apply themselves to heavy workloads, many feel insecure about their skills gaps or undeserving of holding certain positions. A programmer could slip into imposter syndrome after making a coding mistake. Women in technology may also feel they don't belong due to gender inequality in STEM fields. People with imposter syndrome often set impossibly high goals they cannot attain. Self-criticism and disappointment in one's shortcomings can then lead to low productivity, job dissatisfaction, anxiety, and depression. Success in Tech — 6 Ways to Overcome Imposter Syndrome Imposter syndrome affects anyone at any stage of their career. While it’s common, you should take steps to confront this confidence issue head-on, as it can negatively impact your performance and creativity. These six strategies are most effective for overcoming imposter syndrome and regaining self-esteem in the tech sector. 1. Avoid Comparisons If you want to feel bad about yourself, keep comparing your skills and expertise to other people. Current or aspiring technology professionals have a knack for measuring themselves up against their peers. However, each person has undergone different educational, professional, and hands-on experiences to get to where they are — just as you have. Your career journey is your own and is incomparable to other people's paths. 2. Let Go of Perfectionism Indeed, the technology industry is rapidly evolving. To keep up with the pace, you may feel there is little room for mistakes. Perfectionism is a surefire way to falter, though. Failure is inevitable, no matter how hard you try to avoid it. When it happens, simply accept the outcome, reflect on what went wrong, and do better next time. Change your mindset to view mistakes as learning opportunities instead of defeats. This will lead you to improve your technical abilities and job performance in the long run. 3. Draw From Experience Technology professionals are up against a lot. Whether they compare themselves to industry peers or the exceedingly intelligent machines they helped create, it’s easy to see how they would develop imposter syndrome. Believe it or not, you can still outsmart AI. Despite recent advancements, ChatGPT when prompted. Although it can boost productivity and streamline mundane tasks, data scientists find it produces numerous coding errors. delivers misinformation 20% of the time As a machine, it also lacks emotional intelligence. There are human traits and experiences you simply cannot replace. When you feel self-doubt, draw from your knowledge and past experiences and remember your value in the field. 4. Celebrate Achievements Even minor wins are worth celebrating to combat imposter syndrome in tech. Did you discover a new productivity hack? You may have made a significant contribution to a team's innovation. Reflect on your achievements, including work in progress. Other reasons to celebrate include taking risks, experimenting with something new, and, yes, even learning from mistakes. Reward yourself with takeout for dinner, unplug for a relaxing evening, or buy yourself an expensive Starbucks drink on the way to work. 5. Create a Smile File When challenges arise, you may forget to see the big picture. If you aspire to enter the technology industry and get met with rejection letters, you might also consider giving up on your dreams. A " smile file" is a folder you create in your email inbox where you store words of praise and encouragement. Suppose a supervisor, co-worker, or client sent an email thanking you for a job well done. Likewise, a rejection note may include an opportunity for a different position because of your unique skill set — it's still a foot in the door. These smile-worthy messages put things into perspective and are worth saving for a rainy day. 6. Seek Mentorship Innovation is critical for any technology role. When you feel unsure of yourself, seeking mentorship will empower you, boost your confidence, and help you advance in your career. It also drives creative performance. Mentorship stimulates fresh ideas, fosters growth, and encourages out-of-the-box thinking. It is also beneficial for problem-solving and organizational improvements. According to a recent workplace happiness survey, have greater job satisfaction, while 89% said mentorship made them feel more valued at work. 91% of employees with mentors Break Free From Imposter Syndrome You will always face challenges throughout your career. Likewise, imposter syndrome will creep up on occasion in the tech field. Garner whatever tools you need to step into your spotlight and become the innovator you know you can be.

Battling Imposter Syndrome in Tech

If something is valuable, you need to protect it with trustworthy shields. This holds true for both tangible and digital assets. With hackers and malicious software constantly weaving their way through the web, cybersecurity specialists are on a mission to develop next-gen hazard mitigation methods to keep digital threats at bay. Durga Prasada Rao Sanagana, the lead cybersecurity architect at a renowned financial organization, is among the tech experts stepping up to combat cyber threats with advanced firewall defenses. “As technology for good evolves, so does ill-intentioned technology. Threats evolve faster than ever, so our defenses must keep pace, anticipate the next move, and outsmart the adversary,” says Sanagana, reflecting on the importance of revamping cybersecurity ahead of the curve. The North Star of Cybersecurity Innovation Thanks to his significant career strides, Sanagana has become a notable figure in cybersecurity. As a lead security architect for a major financial services company, Sanagana focuses on safeguarding digital assets and developing new defense methods. Applying his forte to real-life situations, Sanagana has built specialization in a myriad of sectors. This includes cybersecurity architecture and engineering, AI, data science, cloud security & architecture, and DevSecOps automation among others. His significant contributions in retail, banking and finance, pharmaceuticals, and healthcare are evident in the solutions he developed for global giants. His work today directly sparks a movement with the next generation of cybersecurity champions. When he is not actively conceptualizing new fundamentals for online safety, he mentors an international faculty development program and guides 140 faculty members, PhD scholars, and industry professionals in building a resilient cybersecurity architecture for data science operations. Passing the baton of knowledge certifies the continued success of the cybersecurity field. Beyond his direct career-related achievements, Sanagana participates actively in the cybersecurity community. That said, he has taken on an off-duty role as a peer reviewer for Springer and IEEE (Institute of Electrical and Electronics Engineers) conferences and regularly judges hackathons and international award events. Sanagana has also put his prowess to paper through notable pieces in Elsevier and JSTAR. Before and After: Perpetual Cybersecurity Growth In 2023, the cost of cybercrime in the United States saw an immense uptick. The increasing frequency and sophistication of cyber threats drove this significant growth. Sanagana's role is crucial in cultivating this aspect of the digital world. His projects surrounding next-gen firewalls, threat intelligence, threat modeling, and intrusion detection systems are fueled by deep learning techniques to predict and mitigate cyber threats. These initiatives offer a significant edge over traditional methods. Moreover, AI is applicable in nearly every industry, and cybersecurity is no exception. Sanagana recognizes the power of AI and stands at the forefront of integrating it into cybersecurity. The architect’s AI-driven adaptive learning systems for business intelligence simulation structures use machine learning and data analytics to enhance threat detection and proactive defense mechanisms. Locked and Loaded: 21st Century Cybersecurity The Internet of Things (IoT) is expanding by the minute, giving cyber threats a more spacious platform to attack. Sanagana envisions a multi-layered cybersecurity method combining AI, blockchain, and quantum computing in a solid attempt to thwart this. As cyber threats grow in complexity, sophisticated defenses are indispensable. Sanagana's work personifies the forward-thinking perspectives required to secure humanity's digital future. “The cyber threats we face in the present are in a constant state of change. The forms they take today may be completely unrecognizable by tomorrow. Our response must be equally innovative, integrating the best of technology and human ingenuity,” he concludes, highlighting his commitment to a safer digital landscape. Building next-gen firewalls that keep assets safe and hackers out; this is the focal point of Durga Prasada Rao Sanagana's work. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here. If something is valuable, you need to protect it with trustworthy shields. This holds true for both tangible and digital assets. With hackers and malicious software constantly weaving their way through the web, cybersecurity specialists are on a mission to develop next-gen hazard mitigation methods to keep digital threats at bay. If something is valuable, you need to protect it with trustworthy shields. This holds true for both tangible and digital assets. With hackers and malicious software constantly weaving their way through the web, cybersecurity specialists are on a mission to develop next-gen hazard mitigation methods to keep digital threats at bay. Durga Prasada Rao Sanagana , the lead cybersecurity architect at a renowned financial organization, is among the tech experts stepping up to combat cyber threats with advanced firewall defenses. Durga Prasada Rao Sanagana Durga Prasada Rao Sanagana “As technology for good evolves, so does ill-intentioned technology. Threats evolve faster than ever, so our defenses must keep pace, anticipate the next move, and outsmart the adversary,” says Sanagana, reflecting on the importance of revamping cybersecurity ahead of the curve. “As technology for good evolves, so does ill-intentioned technology. Threats evolve faster than ever, so our defenses must keep pace, anticipate the next move, and outsmart the adversary,” The North Star of Cybersecurity Innovation The North Star of Cybersecurity Innovation Thanks to his significant career strides, Sanagana has become a notable figure in cybersecurity. As a lead security architect for a major financial services company, Sanagana focuses on safeguarding digital assets and developing new defense methods. Applying his forte to real-life situations, Sanagana has built specialization in a myriad of sectors. This includes cybersecurity architecture and engineering, AI, data science, cloud security & architecture, and DevSecOps automation among others. His significant contributions in retail, banking and finance, pharmaceuticals, and healthcare are evident in the solutions he developed for global giants. His work today directly sparks a movement with the next generation of cybersecurity champions. When he is not actively conceptualizing new fundamentals for online safety, he mentors an international faculty development program and guides 140 faculty members, PhD scholars, and industry professionals in building a resilient cybersecurity architecture for data science operations. Passing the baton of knowledge certifies the continued success of the cybersecurity field. Beyond his direct career-related achievements, Sanagana participates actively in the cybersecurity community. That said, he has taken on an off-duty role as a peer reviewer for Springer and IEEE (Institute of Electrical and Electronics Engineers) conferences and regularly judges hackathons and international award events. Sanagana has also put his prowess to paper through notable pieces in Elsevier and JSTAR. Before and After: Perpetual Cybersecurity Growth Before and After: Perpetual Cybersecurity Growth In 2023, the cost of cybercrime in the United States saw an immense uptick. The increasing frequency and sophistication of cyber threats drove this significant growth. Sanagana's role is crucial in cultivating this aspect of the digital world. His projects surrounding next-gen firewalls, threat intelligence, threat modeling, and intrusion detection systems are fueled by deep learning techniques to predict and mitigate cyber threats. These initiatives offer a significant edge over traditional methods. Moreover, AI is applicable in nearly every industry, and cybersecurity is no exception. Sanagana recognizes the power of AI and stands at the forefront of integrating it into cybersecurity. The architect’s AI-driven adaptive learning systems for business intelligence simulation structures use machine learning and data analytics to enhance threat detection and proactive defense mechanisms. Locked and Loaded: 21st Century Cybersecurity Locked and Loaded: 21st Century Cybersecurity The Internet of Things (IoT) is expanding by the minute, giving cyber threats a more spacious platform to attack. Sanagana envisions a multi-layered cybersecurity method combining AI, blockchain, and quantum computing in a solid attempt to thwart this. As cyber threats grow in complexity, sophisticated defenses are indispensable. Sanagana's work personifies the forward-thinking perspectives required to secure humanity's digital future. “The cyber threats we face in the present are in a constant state of change. The forms they take today may be completely unrecognizable by tomorrow. Our response must be equally innovative, integrating the best of technology and human ingenuity,” he concludes, highlighting his commitment to a safer digital landscape. “The cyber threats we face in the present are in a constant state of change. The forms they take today may be completely unrecognizable by tomorrow. Our response must be equally innovative, integrating the best of technology and human ingenuity,” Building next-gen firewalls that keep assets safe and hackers out; this is the focal point of Durga Prasada Rao Sanagana's work. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here . here

Tech Expert Durga Sanagana Advances Next-Gen Firewalls and Threat Modeling Techniques

টেক এক্সপার্ট দুর্গা সনাগানা নেক্সট-জেন ফায়ারওয়াল এবং থ্রেট মডেলিং কৌশলকে অগ্রসর করেছে

Tech-Expertin Durga Sanagana entwickelt Firewalls und Bedrohungsmodellierungstechniken der nächsten Generation weiter

El experto en tecnología Durga Sanagana avanza en las técnicas de modelado de amenazas y firewalls de próxima generación

L'expert technique Durga Sanagana fait progresser les pare-feu de nouvelle génération et les techniques de modélisation des menaces

तकनीकी विशेषज्ञ दुर्गा सनागना ने अगली पीढ़ी की फायरवॉल और खतरा मॉडलिंग तकनीक विकसित की

技術専門家のドゥルガ・サナガナが次世代ファイアウォールと脅威モデリング技術を進化させる

기술 전문가 Durga Sanagana, 차세대 방화벽 및 위협 모델링 기술 발전

A especialista em tecnologia Durga Sanagana desenvolve firewalls de última geração e técnicas de modelagem de ameaças

Технический эксперт Дурга Санагана продвигает межсетевые экраны нового поколения и методы моделирования угроз

Teknoloji Uzmanı Durga Sanagana Yeni Nesil Güvenlik Duvarlarını ve Tehdit Modelleme Tekniklerini Geliştiriyor

Chuyên gia công nghệ Durga Sanagana nâng cao kỹ thuật mô hình hóa mối đe dọa và tường lửa thế hệ tiếp theo

技术专家 Durga Sanagana 推进下一代防火墙和威胁建模技术

Learning how to use AI tools using plots and examples from science fiction movies. 77% of people fear they could lose their jobs to artificial intelligence in the next 12 months. These are the results of a July 2023 study published by Forbes. It is a great demonstration of people's propensity for irrational fears. Particularly, when people have been scared for a long time with stories about Skynet, TechnoCore, and similar soulless, but invariably bloodthirsty, AI. In reality, there are no streams of ex-employees leaving their offices with boxes of personal belongings in their hands. Among our colleagues, acquaintances, and clients, there is not a single person who has fired an employee or lost his or her job because artificial intelligence has replaced them. On the contrary, there is a consistent shortage of qualified personnel. This includes those who are able to effectively search for and use various AI applications in their work. There is a very real, factual problem that we want to dedicate this article to. While working on our Pitch Avatar, we have more than once encountered complaints about the difficulties of working with AI tools. To summarize, they boil down to the fact that many users have found their expectations of AI to be unrealistic. "I thought it would be smarter," one of our acquaintances irritatedly remarked, sharing his experience with one popular AI chatbot that he used as a text generator and editor. And he is, to repeat, not alone. Notorious "hallucinations", errors, and multiple repetitions or banalities are only part of the problem. Much worse is the fact that people often fail to find a "common language" with AI. The application, which purports to be trained to perceive natural speech, nevertheless struggles to understand what users want from it, interpreting its requests in its own way time after time. In turn, the human user is repeatedly frustrated in their attempts to tell the AI what it wants... Have you experienced any of the above? If so, then welcome aboard. In search of answers and solutions to these problems, we embark on a journey through the plots of sci-fi movies. The Brute Force Method To begin with, while seemingly similar, AI-based tools, even those designed to accomplish the same goals, are still different. You shouldn't conclude that AI can't handle your tasks based on one or two solutions. Even if they all look the same to you. Try to act like Detective Del Spooner from Alex Proyas' movie I, Robot. He believed in the existence of a unique robot among serial models, persistently searched for it, and managed to find it. Moreover, in the end, this robot turned out to be the tool that helped the detective to fulfill a supremely difficult task - to defeat an out-of-control artificial intelligence, V.I.K.I. So be persistent. If you must - explore the myriad possibilities and try dozens, if necessary, of different AI tools in your work. Almost certainly you will find one to your liking. Exclusion Method Imagine that you have formulated a task for your AI (for example, to find some article or create some content), and it "mined" or generated something that technically meets your request, but is not what you wanted. At the same time, for various reasons, you can't clarify your query or reformulate it. This may be because you lack sufficient information. What to do? Skynet and the Terminator in James Cameron's movie were in the same situation. Remember, they didn't know exactly which Sarah Connor from Los Angeles they wanted. So the Terminator decided to "visit" all the Sarah Connors in Los Angeles by simply moving through all of the Sarah Connors listed in the phone book. Makes sense, doesn't it? This is not a bad solution in situations where the AI responding to your query is not giving you the result you want. If you have time, you can simply be patient and methodically repeat your query, specifying that the previous results are not suitable. Sooner or later, the AI will hit the target. A Method of Checking for Complexity and Contradictions Anyone who has worked with AI has encountered the proverbial machine hallucinations and delusions. This includes all of those situations where text generators write incoherent nonsense, AI analytics show calendar data from a century ago, universal AI assistants create empty references and invent quotes from non-existent people, and image generators offer a maze of disfigured people, skewed buildings, and insane landscapes. However, before you rage at your AI assistants, consider why this is happening. Let's reveal a terrible secret: Artificial intelligence isn't trying to piss you off. It's trying to please you by producing the most satisfying result possible. But all AI is limited in terms of resources, time, and skill. That is why AI always tries to follow the path of least resistance. And if a task exceeds its capabilities for one reason or another, it starts to "rave", simplifying the task to a level that allows it to fit into the Procrustean bed of its skills and resources. Think of the classic example from Stanley Kubrick's movie 2001: A Space Odyssey, made in 1968. In it, HAL 9000, a very smart AI that is installed in a spaceship was given a task that contradicted its basic programming. As a result, it "went mad" and decided to eliminate the contradiction by destroying the crew... When faced with signs of "madness" in your AI tools, think about whether your task is ambiguous or contradictory. Can it be formulated more clearly, specifically, and as a result, more simply? Keep in mind - "simpler" does not always mean "shorter". Sometimes, in order to make the task unambiguous, you have to spend not less, but more words for clarification. A Method for Identifying Major and Minor Another common problem is that AI focuses on secondary tasks rather than primary ones. Let's say you need a text about otters living in a lake. And you instruct the AI writer to generate it. But as a result, it produces a narrative 90% is dedicated to the lake itself and only 10% to otters. And the story about the lake, in its turn, is full of details that have nothing to do with nature at all. For example, human settlements located on its shores and their history. Something similar is demonstrated by such movie characters as the protocol robot C3PO from the epic Star Wars or the android Data from the Star Trek: The Next Generation series. They would, occasionally, start sharing information not directly related to the solution for which they had been approached. Other characters were often forced to either clarify the task at hand or simply interrupt the stream of consciousness from the AI. From these examples, it is clear that when formulating tasks for AI, one should clearly prioritize them. A good example is the straightforward order given to the android Ash in Ridley Scott's movie, Alien. Ash was tasked with ensuring the delivery of an alien organism to Earth. It was explicitly specified that the survival of the crew of the ship Nostromo, which included him, could be neglected. Though Ash himself even admitted to sympathizing with the humans he served with, orders were orders and he followed them diligently. Human-AI System, or the Inevitable "42" It is time for the main conclusions of this text. Let's ask ourselves - why, for all their logic, did Skynet and Terminator, HAL 9000 and Ash, and many other movie-villain AIs fail? It goes without saying that "Because the script said so" is not an acceptable answer. The correct answer is because all of these AIs opposed humans, rather than cooperating with them. And this is the point of any artificial intelligence’s basic philosophy. It is not designed to work independently but rather to interact with humans. If you will, any artificial intelligence is a part of a "Human-AI" system. Outside of this system, it is incomplete. For AI to work effectively, it will always need people to give it tasks, as well as to edit, correct, and refine the results of its work. As a consequence, as we develop artificial intelligence we will constantly need to develop our skills in working with it. AI mistakes are inevitable because they are inevitable in humans. It is important to keep in mind that AI is learning and developing. And, as we know, it is impossible to travel this path without mistakes and failures. That's why we should always be ready to hear this answer from AI: "42" The article was created in collaboration with Andriy Tkachenko. Feature (C) Image created with Bing Learning how to use AI tools using plots and examples from science fiction movies. Learning how to use AI tools using plots and examples from science fiction movies. 77% of people fear they could lose their jobs to artificial intelligence in the next 12 months. These are the results of a July 2023 study published by Forbes . It is a great demonstration of people's propensity for irrational fears. Particularly, when people have been scared for a long time with stories about Skynet, TechnoCore, and similar soulless, but invariably bloodthirsty, AI. published by Forbes published by Forbes In reality, there are no streams of ex-employees leaving their offices with boxes of personal belongings in their hands. Among our colleagues, acquaintances, and clients, there is not a single person who has fired an employee or lost his or her job because artificial intelligence has replaced them. On the contrary, there is a consistent shortage of qualified personnel. This includes those who are able to effectively search for and use various AI applications in their work. There is a very real, factual problem that we want to dedicate this article to. While working on our Pitch Avatar , we have more than once encountered complaints about the difficulties of working with AI tools. To summarize, they boil down to the fact that many users have found their expectations of AI to be unrealistic. Pitch Avatar Pitch Avatar "I thought it would be smarter," one of our acquaintances irritatedly remarked, sharing his experience with one popular AI chatbot that he used as a text generator and editor. And he is, to repeat, not alone. Notorious "hallucinations", errors, and multiple repetitions or banalities are only part of the problem. Much worse is the fact that people often fail to find a "common language" with AI. The application, which purports to be trained to perceive natural speech, nevertheless struggles to understand what users want from it, interpreting its requests in its own way time after time. In turn, the human user is repeatedly frustrated in their attempts to tell the AI what it wants... Have you experienced any of the above? If so, then welcome aboard. In search of answers and solutions to these problems, we embark on a journey through the plots of sci-fi movies. The Brute Force Method To begin with, while seemingly similar, AI-based tools, even those designed to accomplish the same goals, are still different. You shouldn't conclude that AI can't handle your tasks based on one or two solutions. Even if they all look the same to you. Try to act like Detective Del Spooner from Alex Proyas' movie I, Robot. He believed in the existence of a unique robot among serial models, persistently searched for it, and managed to find it. Moreover, in the end, this robot turned out to be the tool that helped the detective to fulfill a supremely difficult task - to defeat an out-of-control artificial intelligence, V.I.K.I. So be persistent. If you must - explore the myriad possibilities and try dozens, if necessary, of different AI tools in your work. Almost certainly you will find one to your liking. Exclusion Method Imagine that you have formulated a task for your AI (for example, to find some article or create some content), and it "mined" or generated something that technically meets your request, but is not what you wanted. At the same time, for various reasons, you can't clarify your query or reformulate it. This may be because you lack sufficient information. What to do? Skynet and the Terminator in James Cameron's movie were in the same situation. Remember, they didn't know exactly which Sarah Connor from Los Angeles they wanted. So the Terminator decided to "visit" all the Sarah Connors in Los Angeles by simply moving through all of the Sarah Connors listed in the phone book. Makes sense, doesn't it? This is not a bad solution in situations where the AI responding to your query is not giving you the result you want. If you have time, you can simply be patient and methodically repeat your query, specifying that the previous results are not suitable. Sooner or later, the AI will hit the target. A Method of Checking for Complexity and Contradictions Anyone who has worked with AI has encountered the proverbial machine hallucinations and delusions. This includes all of those situations where text generators write incoherent nonsense, AI analytics show calendar data from a century ago, universal AI assistants create empty references and invent quotes from non-existent people, and image generators offer a maze of disfigured people, skewed buildings, and insane landscapes. However, before you rage at your AI assistants, consider why this is happening. Let's reveal a terrible secret: Artificial intelligence isn't trying to piss you off. It's trying to please you by producing the most satisfying result possible. But all AI is limited in terms of resources, time, and skill. That is why AI always tries to follow the path of least resistance. And if a task exceeds its capabilities for one reason or another, it starts to "rave", simplifying the task to a level that allows it to fit into the Procrustean bed of its skills and resources. Think of the classic example from Stanley Kubrick's movie 2001: A Space Odyssey, made in 1968. In it, HAL 9000, a very smart AI that is installed in a spaceship was given a task that contradicted its basic programming. As a result, it "went mad" and decided to eliminate the contradiction by destroying the crew... When faced with signs of "madness" in your AI tools, think about whether your task is ambiguous or contradictory. Can it be formulated more clearly, specifically, and as a result, more simply? Keep in mind - "simpler" does not always mean "shorter". Sometimes, in order to make the task unambiguous, you have to spend not less, but more words for clarification. A Method for Identifying Major and Minor Another common problem is that AI focuses on secondary tasks rather than primary ones. Let's say you need a text about otters living in a lake. And you instruct the AI writer to generate it. But as a result, it produces a narrative 90% is dedicated to the lake itself and only 10% to otters. And the story about the lake, in its turn, is full of details that have nothing to do with nature at all. For example, human settlements located on its shores and their history. Something similar is demonstrated by such movie characters as the protocol robot C3PO from the epic Star Wars or the android Data from the Star Trek: The Next Generation series. They would, occasionally, start sharing information not directly related to the solution for which they had been approached. Other characters were often forced to either clarify the task at hand or simply interrupt the stream of consciousness from the AI. From these examples, it is clear that when formulating tasks for AI, one should clearly prioritize them. A good example is the straightforward order given to the android Ash in Ridley Scott's movie, Alien. Ash was tasked with ensuring the delivery of an alien organism to Earth. It was explicitly specified that the survival of the crew of the ship Nostromo, which included him, could be neglected. Though Ash himself even admitted to sympathizing with the humans he served with, orders were orders and he followed them diligently. Human-AI System, or the Inevitable "42" It is time for the main conclusions of this text. Let's ask ourselves - why, for all their logic, did Skynet and Terminator, HAL 9000 and Ash, and many other movie-villain AIs fail? It goes without saying that "Because the script said so" is not an acceptable answer. The correct answer is because all of these AIs opposed humans, rather than cooperating with them. And this is the point of any artificial intelligence’s basic philosophy. It is not designed to work independently but rather to interact with humans. If you will, any artificial intelligence is a part of a "Human-AI" system. Outside of this system, it is incomplete. For AI to work effectively, it will always need people to give it tasks, as well as to edit, correct, and refine the results of its work. As a consequence, as we develop artificial intelligence we will constantly need to develop our skills in working with it. AI mistakes are inevitable because they are inevitable in humans. It is important to keep in mind that AI is learning and developing. And, as we know, it is impossible to travel this path without mistakes and failures. That's why we should always be ready to hear this answer from AI: "42" The article was created in collaboration with Andriy Tkachenko. The article was created in collaboration with Andriy Tkachenko. Feature (C) Image created with Bing Feature (C) Image created with Bing

A Cinematic Tutorial on How to Work With Artificial Intelligence

Cardiovascular diseases are a significant problem worldwide, with millions dying annually due to these ailments. While technology has improved detection, researchers are taking the next step by employing artificial intelligence (AI) to help patients with coronary heart disease, cerebrovascular disease and other conditions. Can AI detect cardiovascular disease? Here’s how AI will affect the future of cardiovascular healthcare. Can AI Detect Cardiovascular Disease? The medical field has rapidly adopted AI to improve healthcare outcomes, with hospitals finding ways to integrate it into daily operations. Here are four recent innovations helping researchers equip this advanced technology and identify heart disease. Analyzing Genes In early 2023, researchers from Rutgers University discussing genetic analysis and how AI and machine learning (ML) can identify cardiovascular disease. The Rutgers Institute for Health used AI to examine healthy people and those with heart disease and was able to link genes to cardiovascular issues. published a study Researchers found correlations with age and gender and concluded the risk of cardiovascular disease increases with age. The Rutgers study trained its model to detect patients at high risk for heart issues. It was able to predict the association with heart failure, atrial fibrillation, and other risks based on demographics. Studying Cells Putting cells under the microscope is another effective way to identify cardiovascular disease using AI. Researchers at the University of East London have wielded AI to and determine who is more at risk. The scientists isolated miniature stem cell hearts and placed them in a petri dish. As they age, the trained model can predict which sets are more at risk and will become faulty. study cells with heart disease While tiny, the stem cells function like a living human heart with a consistent heartbeat. They have heart disease, allowing the East London researchers to train its AI as a diagnostic tool. With this information, the university’s researchers are using AI to understand its connection with aging and improve treatment for those with the disease. Prashant Ruchaya, a senior lecturer at the University of East London, has spearheaded the research and says the technology is crucial for the future of heart disease mitigation. “Cardiovascular disease remains the leading cause of mortality worldwide, and aging is a significant risk factor for its development,” Ruchaya said. “Our research aims to comprehensively explore how aging influences the mechanisms underlying heart diseases, ultimately leading to innovative approaches in mitigating the impact of cardiovascular conditions on older populations. “We are working on cutting-edge research here in developing new disease models and developing AI to predict disease earlier and to develop new treatments to treat heart disease.” Examining MRIs Another AI innovation in the United Kingdom comes from St. Bartholomew’s Hospital in London, where experts have advanced technology to find heart disease more rapidly. The researchers say they can using AI during its pilot, leading to more hospitals using the technology. The National Health Service (NHS) says it will spread the AI-assisted detector to various facilities across its system. detect heart disease in 20 seconds In this instance, the AI examines the MRI in real time, shortening the time necessary for analysis. The NHS says cardiologists need at least 13 minutes to check the MRI scans, so reducing the wait to 20 seconds would drastically reduce this gap and help healthcare providers. Other benefits of this AI system include waiting about 33% less time for results and reducing misclassifications, thus improving treatments. Inspecting Heart Dimensions Healthcare experts also use AI to detect cardiovascular diseases based on the heart’s dimensions. Researchers at Cedars-Sinai Medical Center for detecting diseases — hypertrophic cardiomyopathy and cardiac amyloidosis — you might overlook when examining the heart. Can AI detect a cardiovascular disease that’s difficult to find? have developed an AI tool The researchers trained the AI based on 34,000 ultrasound videos to distinguish various sections of the heart. Identifying the thickness of a heart’s walls and the size of its chambers helps the AI know when abnormalities occur. With this information, healthcare providers will better understand which patients are more at risk for cardiovascular diseases — especially underdiagnosed conditions like hypertrophic cardiomyopathy and cardiac amyloidosis. Implications of Using AI Detection for Heart Disease Cardiologists have opportunities for improved diagnoses and treatments as AI usage increases in healthcare. Here are some of the implications of using AI for detecting cardiovascular diseases. Early Treatment As Ruchaya said, heart disease is the leading cause of death, with the World Health Organization estimating worldwide. Heart attacks and strokes are responsible for more than 80% of deaths, and about one-third occur in people under 70 years old. Early detection of heart disease is crucial, and healthcare providers can achieve diagnoses quicker with AI. nearly 18 million annual deaths AI tools use algorithms with vast amounts of data stored inside their systems. When directed, the AI can reference this historical information to examine a patient’s heart and determine their disease risk. While cardiologists have extensive knowledge, AI and ML can be helpful companions for healthcare professionals in detecting cardiovascular diseases at early stages and increasing accuracy. Increasing Efficiency One goal for using AI is to achieve quicker diagnoses, and researchers also want to improve the accuracy to enhance treatment. Healthcare experts have begun wielding AI to increase precision when examining patients’ hearts and have seen positive results. For example, Mayo Clinic has an AI-assisted screening tool to detect heart problems without apparent symptoms. This system when finding people at risk with these conditions, improving upon other technologies at the clinic. The medical center says it has also integrated AI with Apple Watch to detect when the user has a weak heart pump. has a 93% accuracy rate Personalizing the Treatment Delivering personalized treatment for patients with cardiovascular issues is essential for care, and AI can build upon this to help cardiologists. AI-assisted technology boosts healthcare providers’ assessment of their patients by analyzing vast amounts of data and painting a comprehensive picture of their needs. With more targeted information, they can understand the nuances of their patients’ conditions and more accurately predict future outcomes. Saving Money Using AI for heart disease detection also means saving money in healthcare costs. Achieving early diagnoses means delivering effective treatments and other mitigation tactics to make the disease more manageable throughout your life. Thus, you’ll save money with fewer complications and hospital visits. This advanced technology also presents opportunities for hospitals to save money by allocating more resources to individuals at high risk for other diseases. Experts anticipate AI to be a significant cost-saving mechanism for healthcare, with an Artificial Intelligence in Healthcare study for America’s medical costs. expecting $150 billion in annual savings Research has already demonstrated AI’s potential to save money in healthcare by reducing the time necessary for cardiologists and their patients. For instance, the AI tool examining MRI scans reduces wait times by about 33% compared to previous technologies and drastically cuts the wait for doctors to check them. The Challenges of AI in Heart Disease Detection While AI has revolutionized healthcare in recent years, it presents challenges for the field that providers must deal with for adequate care. Here are some concerns you may see experts raise with AI and heart disease detection: One criticism of AI is its biases, considering where it sources data. The information may derive from outdated sources built on criticized research, making you question its legitimacy. AI developers must ensure the tools use the best information possible. Biases: While accuracy may increase with AI, there is room for improvement. These tools may accidentally provide false positives or negatives, so healthcare providers must be vigilant and scrutinize the AI’s findings. Accuracy: AI relies on large sets of sensitive medical information, making data privacy a concern for healthcare facilities. Cybercriminals often target hospitals for ransomware attacks due to security vulnerabilities and the amount of information stored in their systems. Privacy: AI Can Detect Cardiovascular Disease Accurately The future of healthcare will depend on AI as researchers develop new tools to assist providers. Some of these technologies have already demonstrated their abilities when studying heart conditions. The past few years have given you a glimpse of AI’s extraordinary potential in healthcare, and the future is bright. While more research is necessary, you’ll see cardiologists and other healthcare providers use AI to deliver more personalized care for you. Lead image by the National Cancer Institute on Unsplash

AI May Help Detect Signs of Heart Disease at an Earlier Stage

Online discussions about life in California often paint a grim picture: rampant car break-ins, drug crises, sprawling homeless camps, and a general sense of decay. This begs the question: is the Bay Area, once the world's startup utopia, still the future of tech, or has it become a relic of the past? Enter Helsinki. Tucked away in the northern stretches of Europe, Finland's capital is emerging as a dark horse in the global tech race. This city, known for its tranquil streets and high quality of life, is rapidly becoming a powerhouse in the tech world. But it's not just the clean air and low crime rates that are drawing a crowd; Helsinki is proving to be a fertile ground for technological innovation, particularly in AI, quantum computing, and smart technology. At the heart of this innovation lies , a city marketing, investment, and talent initiative owned by the city of Helsinki. Helsinki is doing everything it can to bring top talent to the country, and the results speak for themselves: Helsinki is the #1 ranked in Europe, boasts the in the world, and its VC funds continue to . Helsinki Partners digital economy best work-life balance perform strongly Helsinki's Technological Prowess: AI and Beyond After decades of research, AI has finally become mainstream, largely thanks to . It seems like every startup these days is AI-focused, and there’s no better place to be than Helsinki. Helsinki is making significant strides in AI, but it's not just about the number of startups or the funding pouring in; it's the quality of research and the ethical approach to technology that sets the city apart. LLMs have received around EUR 200 million in additional funding since 2017. A major factor behind this surge is Finland’s national AI strategy, one of the first in the world, emphasizing education and research in AI. This strategy has cultivated a rich ecosystem where fundamental research meets practical application. Over 300 AI firms The (FCAI) is a testament to this, focusing on human-centric and ethical AI. This approach to AI, where technology serves humanity and not the other way around, could redefine how we integrate AI into our daily lives. Finnish Center for Artificial Intelligence Quantum Computing: The Next Frontier Helsinki is also at the forefront of . Home to some of Europe's most innovative startups in the field, such as IQM and Bluefors, the city offers a complete supply chain for quantum computing, from fundamental research to production. quantum computing With institutions like Aalto University and VTT Technical Research Centre leading the way, Helsinki is not just participating in the quantum race; it's setting the pace. The emphasis on sustainable development in quantum computing is particularly noteworthy. In a world increasingly concerned with environmental impact, Helsinki's focus on developing sustainable quantum technologies sets a global example. This approach aligns with Finland's broader commitment to sustainability, further enhancing the appeal of Helsinki as a tech hub. Fintech Innovation: A Digital Powerhouse The is booming, leveraging Finland's longstanding expertise in ICT & mobile technology. What makes Helsinki particularly attractive for fintech innovation is the complete value chain it offers – from R&D to software development, algorithms, hardware design, and commercialization. Additionally, the city's strong cybersecurity cluster and open data frameworks provide an ideal environment for fintech companies to flourish. fintech sector in Helsinki Helsinki Partners plays a pivotal role in this growth, supporting international companies and investors in finding the best business opportunities in the city. Their focus on collaboration, diversification, and open-source data sharing is fostering an environment where fintech innovation can thrive. Smart Energy: Leading the Green Revolution Helsinki is not just about digital technology; it's also at the forefront of smart energy solutions. The nation’s has spurred innovation in smart grids, energy-efficient buildings, and renewable energy sources. goal to be carbon-neutral by 2035 Helsinki's smart energy ecosystem benefits from the city's strong technical expertise in 5G, AI, and IoT, coupled with liberal energy legislation and a progressive strategy on clean energy. Individual manufacturers are also playing a big role, from to . This collaboration with the private sector is driving advancements in smart energy solutions, positioning Helsinki as a leader in sustainable energy innovation. bio-heating facilities wind energy A Tech Ecosystem Par Excellence What truly sets Helsinki apart is its holistic approach to technology. The city's tech ecosystem is characterized by its collaborative spirit, where corporations, startups, universities, and the public sector work hand-in-hand. This collaboration is evident in areas ranging from AI and quantum computing to fintech and smart energy. The city's focus on ethical AI, sustainable quantum computing, fintech innovation, and smart energy solutions reflects a deep understanding of the role technology plays in society. Helsinki isn’t just developing technology for the sake of it; the city is reimagining how technology can enhance human life, promote sustainability, and drive economic growth. In conclusion, while Silicon Valley has long been the world's tech Mecca, Helsinki is emerging as a new kind of tech hub – one that prioritizes sustainability, ethics, and collaboration. With its cutting-edge innovations and holistic approach, Helsinki is not just competing with the Silicon Valley model; it's redefining what a tech hub can be. It's a place where technology meets humanity, where innovation is driven by a desire to improve lives and protect the planet. In this new era of tech, Helsinki is not just a city to watch; it's a city to emulate.

Why Helsinki Could Be the Next Silicon Valley: A Tech Perspective

This question is asked by every representative of the IT sphere. Last year, we prepared an article about 5+ trends that will be relevant in 2023, and this time, we decided not to break the tradition. Developers, analysts, testers, designers, IT consultants, and HR at Usetech shared their opinions on what technology trends influenced their field in 2023 and what will be relevant in 2024. Please note that your opinion may be different from that of our employees. And this is normal: every specialist has a different view, experience, and approach to work. So, if you have something to add, feel free to write about it in the comments. It will be interesting to look at different points of view and discuss them with colleagues. In this article, we highlighted the key trends of 2023 in development, analytics, testing, DevOps, HR, and design, and Usetech experts talked about the forecasts for 2024, shared analytics, statistics, and a list of useful resources to keep up to date with all the news. According to Sergey Alexandrov, IT consultant at Usetech, the technology sector is rapidly evolving, and 2024 promises to be a landmark year for innovation. As 2023 has shown, one of the key trends is the rapid growth of Artificial Intelligence (AI). McKinsey Global Institute's projections of a possible $4.4 trillion a year increase in corporate profits due to AI underscores its strategic importance. More and more companies are beginning to actively incorporate generative AI into everyday applications. In addition, one important trend, according to Sergey, is the growth of the software as a service (SaaS) market. The Zippia report forecasts indicate its continuous increase, reaching 18% per year. At the same time, Gartner also tells us that companies' spending on software has increased to 12.9% and on data centers to 4.7%, which has become higher than forecasted. "Next year promises a continuation of important trends. Rising costs for IT services and AI-enabled software will drive up security costs and will be a priority for companies. The increasing need for data protection will create a favorable environment for cybersecurity innovation. The field of AI will also remain relevant. It is expected that AI adoption will start to have a significant impact on business processes, changing the approach to automation and improving business efficiency," — says Sergey. — And if you want to keep up to date with the latest technology news, it's important to follow the leading resources. International publications such as TechCrunch and Wired provide an extensive overview of technology news." Some applications of blockchain, according to Sergey, especially in the context of cryptocurrencies, are losing relevance. Scalability issues and high power consumption raise questions about the feasibility of using this technology in some areas. Neural networks rule the world. Perhaps there is no person who has not heard about the development of neural networks this year. And these are not empty words: discussions about the positive and negative impact of neural networks and AI have been active in the technology community this year. For example, Forbes wrote about it, speculating on what the active development of AI and neural networks will lead to. Our colleague, Alexander Sinichkin, Head of the Development Group, notes that neural networks are penetrating everywhere and becoming indispensable assistants for humans. This is a kind of industrial resolution if you can call it that. Alexander notes the demand for neural networks next year as well. Artificial Intelligence does not give up its position, as noted by Stanislav Bykov, Senior Developer at Usetech: "In the field of technology and development this year, I discovered the possibilities of artificial intelligence. Of course, it has been actively developing for several years, and for me, it was somewhere "near.” But having tried its capabilities, I realize that it is a very powerful tool, which, in skillful hands, has almost limitless possibilities. I hope that not only next year but the next few years will see the continued development of artificial intelligence in many areas." And here are the trends and forecasts highlighted by Ilya Smirnov, Head of the AI/ML Department at Usetech: "Right now, we are witnessing a technological breakthrough in the application and updating of LLM models. Every self-respecting large corporation, from the position of a development leader, has either already created and presented its LLM model or plans to do so in the next year, 2024. At the same time, the areas of LLM implementation are already quite numerous. Companies are trying to implement LLM in support of chatbots; there are cases of implementation for generating responses to legal requests. A very good context search engine can be organized on the basis of LLM. Actually, most of the solutions, such as writing documentation and writing simple front-end solutions based on prototypes in Figma, will allow developers to spend more time on correcting texts rather than composing them from scratch. In terms of development, I think that the strongest implementation will be at the level of autotest generation. There are already successfully implemented projects on writing simple code autotests. As a result of this symbiosis of LLM and IT specialist, developers will have more time and opportunity to write effective code or cover more complex logical functionality with tests". Shall we talk about the DevOps sphere? Evgeny Kiselev, Senior DevOps Engineer, notes the following: " — Cloud platforms such as AWS, Azure, GCP, and Alibaba Cloud are becoming increasingly popular among DevOps teams as they offer flexibility, scalability, and cost savings. — Containerization tools such as Deckhouse, Docker, Kubernetes, and OpenShift continue to evolve and improve, becoming more affordable and easier to use. — Artificial intelligence (AI) and Machine Learning (ML) are starting to be incorporated into DevOps to automate tasks, improve code quality, and optimize workflows. You could even say this year has seen a kind of "breakthrough" of this technology. — DevSecOps, which integrates security processes into development and operations, is becoming increasingly important for application and data security. — DevOps tools such as Jenkins, GitLab, Ansible, and Terraform continue to get better and better." Of course, it's hard to make predictions for 2024, as something new is constantly emerging, but we can highlight a couple of points. And you can add to them in the comments: "It is very difficult to predict, however, we can assume that the following trends in DevOps will be in demand: — Automation of application development and deployment processes and the development of Kubernetes, Docker, Helm, and Ansible container orchestration tools - it has been and will remain. — The use of AI and ML to optimize workflows will only get better. — Applying the Cloud-Native concept of easy scalability and portability of applications across platforms. — Implementing security and compliance practices (security, compliance). — Increasing the role of DevOps culture, training, and involving a large number of employees in development processes. — Development of tools to monitor and analyze application performance in real-time. — Improve tools for collaboration and communication between development and operations management teams. — Create tools for the automatic generation of documentation and reports on infrastructure status. — Increased use of hyper-converged infrastructures (HCI) and software-defined networking (SDN)". But there are things that lose their relevance and do not "take root" in the sphere. And Evgeny talks about it: "The approach of using exclusively automated tools for all stages of development and deployment is losing its relevance. It is not always optimal, as in some cases, manual operations can be more efficient and flexible. Rather than abandoning manual operations altogether, DevOps professionals have been looking for ways to integrate them into the development process, enabling better interaction between people and tools." What do you think about this, DevOps professionals? What's new in front-end? Alexander Goncharov, Head of the Development Group at Usetech, tells us the following: "The year 2023 in front-end can perhaps be called the year of management and architecture. Teams are getting larger, projects are getting more complex, and all this needs to be managed productively. Different methodologies and architectural approaches are increasingly used to divide teams into smaller ones by domain and beyond. Compared to last year, more companies and products have adopted the approach of dividing monolithic applications into micro-frontends and then combining them into a whole application or combining existing applications into a meta-application so that the user has a unified experience. It is always more convenient to maintain and develop separate parts by small teams, and with this architectural approach, "conditionally painless" integration of old legacy solutions is also possible. What will happen next year? As I said above — projects are growing and becoming more complex. I think more and more materials will appear on FSD (Feature Sliced Design), and more teams will apply this methodology on projects. It will allow code to be separated and grouped by subject area, which again makes support and development easier, as well as synchronization between teams. I also think more and more projects will switch from Webpack to Vite and Turbopack builders. Because as projects grow, not only the cognitive load on leads and developers increases, but also the load on build systems." Trends in mobile development Speaking about trends in various spheres, we can't miss the mobile development sphere. That's why we turned to Anna Zharkova, Lead Developer at Usetech, and shared her comments with you: "What's new in mobile development? I can mention the following developments: — WWDC 2023. Smart augmented reality helmets, VisionOS. New products Swift 5.9. This year, Apple emphasized the development of augmented reality technologies and presented its new development: a helmet running on VisionOS. Unfortunately, it will not be possible to feel the novelty until 2025. The developers also paid attention to the Swift language. Version 5.9 includes such cool features as macros for code generation and the Observation framework. It is worth noting the SwiftData framework as an analog and replacement for CoreData, interactive iOS widgets. — KMP. Name change from KMM and release to stable. This fall, what many developers and admirers of Kotlin Multiplatform have been waiting for happened. The technology moved to stable and became fully ready for use in Production. It means that many problems were solved: multithreading was improved last year, and this year, there were many improvements in memory handling and garbage collection. Also, one of the goals of the roadmap for next year, the development company to formulate direct interaction between Kotlin and Swift. The change of abbreviation is intended to fix the technology not only for mobile development but also for desktop and web. — Compose Multiplatform Alfa. A very cool and promising technology for creating a common cross-platform UI in KMP applications. The same issues as Jetpack Compose are relevant for it (state saving, performance issues, memory handling, etc.), but it also has its own cross-platform Challenges. — ChatGPT and analogs. Despite the fact that these technologies don't seem to be directly related to mobile development, they are actively used as tools to find solutions and generate and simplify work. The result is largely controversial, as is the fact of sharpening one's codebase in general, but nevertheless, this is becoming more and more popular. What's new in the testing industry? We did not bypass our colleagues from the testing sphere and asked them what they remembered this year and what trends they could highlight in their working sphere. Alexandra Novitskaya, Senior Test Engineer, tells us about new developments in the testing sphere: "Optimization of testing processes allows to significantly reduce the routine part of work in the tasks of a tester, and the improvement of technologies and tools used allows each tester to choose and adapt his tool to the project tasks. In addition, the variety of sources of specialist development allows a tester not only to stay afloat but also to choose new vectors of development in the IT sphere". Next year, in Alexandra's opinion, the first place will be occupied by the integration of AI and ML as tools for analyzing and automating processes and data in them. This will not only prevent errors and improve testing accuracy but also optimize development processes, making them more transparent and controllable. Some more of the trends of the coming year will be: — Shift-Left testing and integration with DevOps as one of the testing quality control tools to improve the interaction between development and testing teams, As well as to accelerate development phases by automating testing processes, continuous integration, and continuous deployment pipelines. — Security and load testing as tools to ensure reliable security and data protection and high system performance to mitigate risks when launching new services and changing the architecture of highly loaded systems. HR trends in 2024: what to expect? The labor market is constantly changing, and HR departments of companies must be ready to quickly adapt to new requirements and needs. Vera Petrovicheva, Team Lead Recruitment, told us what will be relevant in 2024. As you can see from the first point, AI is persistently penetrating (and to some extent, has already penetrated) the HR sphere: "Artificial Intelligence and automation of hiring and HR processes will be one of the trends in the next year. It will reduce the time and costs of routine tasks and improve the accuracy of candidate forecasting. Flexible forms of work, including telecommuting, freelance, and project work, remain in demand among many companies. This allows them to quickly adapt to changing market needs and attract talent from around the world. I can't help but talk about ethics and social responsibility in HR practices. Companies will aim to create a work environment that promotes the well-being and development of their employees while also considering the impact of their activities on the environment and society as a whole. Another thing to note is the increased importance of soft skills and personality traits in hiring and assessing staff. Companies will assess not only professional skills but also leadership skills, communication skills, adaptability, etc. And, of course, staff training and development as a tool to retain talent and increase their productivity. Companies will invest in training their employees so that they can develop and grow with the company. Conclusion The year 2024 promises to be a watershed year in technology. The rise of AI and SaaS are becoming dominant trends, defining the future of business processes and innovation. However, it is important to remember that successful companies are those that adapt to change in time and skillfully use the latest technologies for their development. Also published . here

Forecasting What is Still to Come in Tech in 2024

Zero-Day Vulnerabilities | All-Day Stress Encountering a zero-day vulnerability can feel like navigating through a fog of unknowns. This kind of vulnerability, previously undetected and unaddressed, often brings a mix of anxiety and urgency. Questions whirl through your mind: How severe is it? What havoc can it wreak? Who might exploit it? And crucially, is it lurking in our systems? These initial questions are just the tip of the iceberg. As the reality sinks in – you're facing an unfamiliar foe – the real challenge emerges: "How do I deal with something I've never seen before?" The reactions vary – from the near-panic of tearing one's hair out to the extreme thoughts of obliterating everything to prevent a breach, and in some cases, people simply giving up. But here's the silver lining: while a zero-day might be an unknown threat, our response doesn't require unknown measures. We can leverage our existing knowledge and past experiences in security and incident response to tackle these new threats effectively. Understanding Zero-Day Vulnerabilities Imagine the world of cybersecurity as a high-stakes game of hide and seek, and zero-day vulnerabilities are the ultimate hiders – unseen, unknown, and unpredictably dangerous. These digital gremlins are the nightmares of IT professionals, lurking in the shadows of code and systems. They are the loopholes that nobody knew existed, until, of course, someone malicious discovers and exploits them – and that's when the real game begins. But what makes a zero-day so special and feared? It's the element of surprise. These vulnerabilities are like the plot twists in a thriller movie – you never see them coming until they hit you. When a zero-day attack is revealed, it's akin to a magician revealing his trick – there's awe, shock, and a bit of "how did we not see that coming?" To truly grasp the essence of zero-days, let’s step into the history books of cybersecurity. Remember ? This devious little worm, discovered back in 2010, wreaked havoc on Iran's nuclear facilities. Stuxnet It’s the poster child for zero-day exploits – sneaky, sophisticated, and with a punch that left the world reeling. Stuxnet used not one, but several zero-days to infiltrate and sabotage its target. It’s like finding out the quiet kid in class is actually a chess grandmaster – unexpected and a little bit unsettling. In the grand scheme of things, zero-days are a harsh reminder of our digital vulnerability – the unknown threats that lurk in the shadows, waiting for their moment in the spotlight. They're a testament to the fact that in cybersecurity, sometimes the biggest threat is what you don't know you don't know. But fear not, for as daunting as these hidden threats are, they are not invincible. The Power of Existing Knowledge So, a zero-day has just dropped into your lap like an uninvited guest at a party. Now what? It's easy to feel like you're up the creek without a paddle, but hold on – you've got more tools at your disposal than you might think. First off, let’s demystify this beast. Zero-days aren't some alien technology; they are vulnerabilities, plain and simple. Think of them as the hidden doors in a fortress that the architect forgot about. Now, the key lies in using what we already know about fortresses – or in our case, cybersecurity – to find and secure these doors. It’s like realizing you’ve been holding the treasure map upside down all along. Our past experiences with cybersecurity are like gold. Each past threat, each breach, and every little anomaly we've encountered and resolved – they've all been building up our arsenal. We've learned to monitor, to analyze, to patch, and to respond. These aren’t just random skills; they are the very weapons we need to combat zero-days. It's like being a chef in a kitchen – you might not know what surprise ingredient you'll have to cook with next, but you know your way around the kitchen, and that’s what counts. Consider this: the basic principles of securing a network against known vulnerabilities apply just as effectively to unknown ones. Regular system monitoring, rigorous security protocols, and a culture of constant vigilance – these are your shields against the onslaught of zero-day attacks. It’s like the age-old adage in martial arts – the best form of defense is a strong foundation. In the next sections, we'll dive deeper into the specific tools and strategies that make our existing knowledge a formidable force against the enigmatic zero-days. Remember, in the world of cybersecurity, knowledge isn’t just power – it’s your best defense. Strengthening the Process You've identified the zero-day lurking in your system. Now, comes the critical part: beefing up your defense. Think of this as fortifying your digital castle against the siege of unknown threats. It's not about reinventing the wheel; it's about reinforcing the wheels you already have. First and foremost, let's talk about patch management. In the world of cybersecurity, patches are like the armor plating on your castle walls. When a zero-day is discovered, vendors often release patches to fix the vulnerability. It’s crucial to apply these patches promptly. Think of it as a race against time, with hackers on one side and your patching team on the other. Fast, efficient patch management isn’t just good practice; it’s your knight in shining armor in the fight against zero-days. Next up, we have threat intelligence. Knowledge is power, and in cybersecurity, it’s your eyes and ears on the digital battleground. Staying updated with the latest threat intelligence feeds can give you the upper hand. It’s like having scouts reporting back on enemy movements – the more you know about potential threats, the better prepared you are to defend against them. But here's the kicker: the process of strengthening your defenses is never truly complete. Cybersecurity is a dynamic field, constantly evolving. This means your strategies and processes must evolve too. Regular security assessments and audits are crucial. They help you identify potential weaknesses in your defenses – the chinks in your armor, so to speak – before they can be exploited. In essence, strengthening your process against zero-days is about being proactive rather than reactive. It's about building a culture of continuous improvement, where your defenses are always one step ahead of potential threats. In the next section, we'll explore the human factor – how training, awareness, and a robust cybersecurity culture can be your strongest allies in this ongoing war against digital threats. The Human Factor At the heart of every great cybersecurity strategy lies its people. The human element in managing zero-days is akin to the knights in the cybersecurity kingdom. It's about more than just skills and knowledge; it's about cultivating a mindset. Training and awareness are the twin pillars of this approach. Imagine every member of your team as a sentry, vigilant and ready. Regular training ensures that everyone is not just aware of the standard protocols but also the latest threats – including zero-days. It's like each person holds a piece of the puzzle; when put together, you get the full picture of your security posture. Creating a security-minded culture within your organization is akin to building a fortress with a strong foundation. It's not just about the tools and technologies; it's about how people think and act every day. Encouraging open communication about potential threats, fostering an environment where security is everyone’s responsibility, and rewarding proactive security behaviors – these are the building blocks of a resilient cybersecurity culture. Moreover, collaboration extends beyond the walls of your organization. Engaging with the wider cybersecurity community is crucial. Sharing experiences, learning from others’ incidents, and staying informed about emerging threats – this collective wisdom is a treasure trove in the fight against zero-days. Think of it as a council of cybersecurity wizards, each bringing their own magic (aka expertise) to the table. Wrapping This Up In our journey through the mysterious and often intimidating world of zero-day vulnerabilities, we've uncovered a fundamental truth: . Like a well-navigated labyrinth, the path to managing zero-days becomes clearer when we use the light of our existing knowledge, tools, and experiences. these unknown threats are not as invincible as they may seem We've seen how understanding zero-days, leveraging our existing expertise, adapting our tools, and continuously strengthening our processes form the bedrock of an effective defense strategy. But, perhaps most importantly, we've recognized the invaluable role of the human element – the vigilance, the culture, and the community collaboration that turn our collective efforts into an impenetrable shield against these hidden digital threats. As we part ways in this discussion, remember that in the ever-changing landscape of cybersecurity, adaptation and resilience are your truest allies. Zero-day vulnerabilities, daunting as they are, offer us opportunities to learn, grow, and fortify our defenses. So, as you navigate this challenging terrain, carry with you the knowledge that with the right approach, even the unknown can be tamed.

NEXXT INTELLIGENCE

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-- employees

Since n.d.

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NEXXT INTELLIGENCE

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