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- 2. What Is Artificial Intelligence? o Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. o AI is an interdisciplinary science with multiple approaches, advancements in machine learning and deep learning, in particular, are creating a paradigm shift in virtually every sector of the tech industry. o Developers use artificial intelligence to more efficiently perform tasks that are otherwise done manually, connect with customers, identify patterns, and solve problems. To get started with AI, developers should have a background in mathematics and feel comfortable with algorithms o In many cases, humans will supervise an AI’s learning process, reinforcing good decisions and discouraging bad ones. But some AI systems are designed to learn without supervision — for instance, by playing a video game over and over until they eventually figure out the rules and how to win. o AI is a concept that has been around, formally, since the 1950s
- 3. What is super AI? o Artificial super intelligence (ASI) is a system that wouldn't only rock humankind to its core, but could also destroy it. If that sounds straight out of a science fiction novel, it's because it kind of is: ASI is a system where the intelligence of a machine surpasses all forms of human intelligence, in all aspects, and outperforms humans in every function. o An intelligent system that can learn and continuously improve itself is still a hypothetical concept. However, it's a system that, if applied effectively and ethically, could lead to extraordinary progress and achievements in medicine, technology, and much more.
- 4. Strong AI Vs. Weak AI Intelligence is tricky to define, which is why AI experts typically distinguish between strong AI and weak AI. Strong AI Strong AI, also known as artificial general intelligence, is a machine that can solve problems it’s never been trained to work on — much like a human can. This is the kind of AI we see in movies, like the robots from Westworld or the character Data from Star Trek: The Next Generation. This type of AI doesn’t actually exist yet. Weak AI Weak AI, sometimes referred to as narrow AI or specialized AI, operates within a limited context and is a simulation of human intelligence applied to a narrowly defined problem Weak AI is often focused on performing a single task extremely well. While these machines may seem intelligent, they operate under far more constraints and limitations than even the most basic human intelligence. Weak AI examples: Siri, Alexa and other smart assistants ,Self-driving cars, Google search, Conversational bots, Email spam filters, Netflix's recommendations
- 5. Ready-to-Use AI Is Making Operationalizing AI Easier The emergence of AI-powered solutions and tools means that more companies can take advantage of AI at a lower cost and in less time. Ready-to-use AI refers to the solutions, tools, and software that either have built-in AI capabilities or automate the process of algorithmic decision-making. Ready-to-use AI includes self-repairing autonomous databases and premade models for image recognition and text analysis on various datasets. How to Get Started with AI Communicate with customers through chatbots. Chatbots use natural language processing to understand customers and allow them to ask questions and get information. These chatbots learn over time so they can add greater value to customer interactions. Monitor your data center. IT operations can streamline monitoring with a cloud platform that integrates all data and automatically tracks thresholds and anomalies. Perform business analysis without an expert. Analytic tools with a visual user interface allow nontechnical people to easily query a system and get an understandable answer.
- 6. • The Four Types of AI AI can be divided into four categories, based on the type and complexity of the tasks a system is able to perform. They are: Reactive machines Limited memory Theory of mind Self awareness
- 7. Reactive Machines A reactive machine follows the most basic of AI principles and, as its name implies, is capable of only using its intelligence to perceive and react to the world in front of it. A reactive machine cannot store a memory and, as a result, cannot rely on past experiences to inform decision making in real time. reactive machines are designed to complete only a limited number of specialized duties. This type of AI will be more trustworthy and reliable, and it will react the same way to the same stimuli every time. Reactive Machine Examples Deep Blue was designed by IBM in the 1990s as a chess-playing supercomputer Google’s AlphaGo is also incapable of evaluating future moves but relies on its own neural network to evaluate developments of the present game
- 8. Limited Memory Limited memory AI has the ability to store previous data and predictions when gathering information and weighing potential decisions — essentially looking into the past for clues on what may come next. Limited memory AI is more complex and presents greater possibilities than reactive machines. Limited memory AI is created when a team continuously trains a model in how to analyse and utilize new data When utilizing limited memory AI in ML, six steps must be followed: • Establish training data • Create the machine learning model • Ensure the model can make predictions • Ensure the model can receive human or environmental feedback Store human and environmental feedback as data Reiterate the steps above as a cycle
- 9. Theory of Mind Theory of mind is just that — theoretical. We have not yet achieved the technological and scientific capabilities necessary to reach this next level of AI. The concept is based on the psychological premise of understanding that other living things have thoughts and emotions that affect the behaviour of one’s self. In terms of AI machines, this would mean that AI could comprehend how humans, animals and other machines feel and make decisions through self-reflection and determination, and then utilize that information to make decisions of their own.
- 10. Self Awareness Once theory of mind can be established, sometime well into the future of AI, the final step will be for AI to become self-aware. This kind of AI possesses human-level consciousness and understands its own existence in the world, as well as the presence and emotional state of others. It would be able to understand what others may need based on not just what they communicate to them but how they communicate it. Self-awareness in AI relies both on human researchers understanding the premise of consciousness and then learning how to replicate that so it can be built into machines.
- 11. Artificial Intelligence Examples ChatGPT ChatGPT is an artificial intelligence chatbot capable of producing written content in a range of formats, from essays to code and answers to simple questions. Launched in November 2022 by OpenAI, ng. Google Maps Google Maps uses location data from smartphones, as well as user-reported data on things like construction and car Smart Assistants Personal assistants like Siri, Alexa and Cortana use natural language processing, or NLP, to receive instructions from users to set reminders, search for online information and control the lights in people’s homes.
- 12. Snapchat Filters Snapchat filters use ML algorithms to distinguish between an image’s subject and the background, track facial movements and adjust the image on the screen based on what the user is doing. Self-Driving Cars Self-driving cars are a recognizable example of deep learning, since they use deep neural networks to detect objects around them, determine their distance from other cars, identify traffic signals and much more. Wearables The wearable sensors and devices used in the healthcare industry also apply deep learning to assess the health condition of the patient, including their blood sugar levels, blood pressure and heart rate. They can also derive patterns from a patient’s prior medical data and use that to anticipate any future health conditions. MuZero MuZero, a computer program created by DeepMind, is a promising frontrunner in the quest to achieve true artificial general intelligence. It has managed to master games it has not even been taught to play, including chess and an entire suite of Atari games, through brute force, playing games millions of times.
- 13. How will AI change the world? • Artificial intelligence has the power to change the way we work, our health, how we consume media and get to work, our privacy, and more. • Consider the impact that certain AI systems can have on the world as a whole. People can ask a voice assistant on their phones to hail rides from autonomous cars to get them to work, where they can use AI tools to be more efficient than ever before. • Doctors and radiologists could make cancer diagnoses using fewer resources, spot genetic sequences related to diseases, and identify molecules that could lead to more effective medications, potentially saving countless lives. • Alternatively, it's worth considering the disruption that could result from having neural networks that can create realistic images, such as Dall-E 2, Midjourney, and Bing; that can replicate someone's voice or create deepfake videos using a person's resemblance. These could threaten what photos, videos, or audios people can consider genuine. • Another ethical issue with AI concerns facial recognition and surveillance, and how this technology could be an intrusion on people's privacy, with many experts looking to ban it altogether.
- 14. The Evolving Stages of Artificial Intelligence • Artificial intelligence can be allowed to replace a whole system, making all decisions end-to-end, or it can be used to enhance a specific process. A standard warehouse management system, for example, can show the current levels of various products, while an intelligent one could identify shortages, analyze the cause and its effect on the overall supply chain and even take steps to correct it. • The demand for faster, more energy-efficient information processing is growing exponentially as AI becomes more prevalent in business applications. Conventional digital processing hardware cannot keep up with this demand. That is why researchers are taking inspiration from the brain and considering alternative architectures in which networks of artificial neurons and synapses process information with high speed and adaptive learning capabilities in an energy-efficient, scalable manner.
- 15. Application of AI AI in the Enterprise Enterprises are primarily using AI to: Detect and deter security intrusions Resolve users’ technology issues Reduce production management work Gauge internal compliance in using approved vendors Best Practices for Getting the Most from AI Apply AI capabilities to those activities that have the greatest and most immediate impact on revenue and cost. Use AI to boost productivity with the same number of people, rather than eliminating or adding headcount. Begin your AI implementation in the back office, not the front office (IT and accounting will benefit the most).
- 16. Understanding AI in Smart Construction • Artificial Intelligence in the construction industry is undergoing a digital transformation. Focussing on technologies like artificial intelligence and machine learning at every stage of engineering and construction, from design to preconstruction to construction to operations and asset management, is exploiting the potential of the construction industry to new levels. • The areas where artificial intelligence in the construction industry is bringing impactful difference by getting the tasks done in a lesser amount of time and in a cost-effective manner. • Planning and designing sub-segment of construction are expected to benefit the most. In the global construction industry, the Europe market is anticipated to top the growth rate. • This technological shift is set to positively impact all the stakeholders across the project – including contractors, owners, and service providers. With other adjacent industries such as transportation and manufacturing having already started working as an ecosystem, it becomes all the more important for the construction industry to adapt to the digitization of the processes.
- 17. • As the technological shift is at a nascent stage in the engineering and construction industry, it will be advantageous for the companies that upgrade the technology. With artificial intelligence in construction, companies can comfortably tackle current issues while avoiding past mistakes. • With the use of statistical techniques of machine learning in construction, it becomes much more convenient and less time-consuming to scrutinize the data pertaining to changed orders, information requests, etc. This will help in proactively alerting the project leaders about the things that need critical attention. Safety monitoring also can be done with more efficiency Examples of Artificial Intelligence in Construction • Planning and Designing through Generative Design • Measuring Site Progress • Robust Fleet Management • Creating Safer Job Sites • Alleviate Labor Shortage • Enhance Project Design Process with AI-powered Insights • Integrate AI Automation in the Project Management Workflow • Collecting and Analyzing the Data Collected from Job Site • Increase Productivity with AI-driven Vehicles • Perform Land Survey and Mapping with Geospatial AI and Drones
- 18. What are the uses of AI in robotics? • This discipline has developed according to the needs that have arisen, but broadly speaking, its benefits are focused in particular on the automation of tasks that provide little value, that may pose a danger to people because they are carried out in hazardous environments, or that require high precision in a repetitive manner and at high speed. • Robotics is also used in other sectors such as healthcare for remote, high-precision operations or laboratory work. • Using appropriate algorithms to detect and manipulate objects, calculate distances and avoid obstacles. • These machines can create maps of their environment and move around without any problem, even in dangerous or inaccessible environments. • They do not need human intervention because they also include the use of Machine Learning. The same is true for the manipulation of objects. • The use of this technology brings precision and efficiency as the sensors provide the necessary information to adapt the grip force according to the object they are handling and the activity they are carrying out. Object manipulation skills also improve as the robot gains experience. It should be remembered that these are tools designed to collaborate with humans and interaction with them is increasing, and they can adapt and In addition, AI is also being used to increase the capabilities of these tools so that they can perform increasingly complex tasks
- 19. How AI Technology Can Help Organizations The central tenet of AI is to replicate—and then exceed—the way humans perceive and react to the world. It’s fast becoming the cornerstone of innovation. Powered by various forms of machine learning that recognize patterns in data to enable predictions, AI can add value to your business by Providing a more comprehensive understanding of the abundance of data available Relying on predictions to automate excessively complex or mundane tasks What's Driving AI Adoption? Three factors are driving the development of AI across industries. Affordable, high-performance computing capability is readily available. The abundance of commodity compute power in the cloud enables easy access to affordable, high-performance computing power. Before this development, the only computing environments available for AI were non-cloud-based and cost prohibitive. Large volumes of data are available for training. AI needs to be trained on lots of data to make the right predictions. Ease of data labeling and affordable storage and processing of structured and unstructured data is enabling more algorithm building and training. Applied AI delivers a competitive advantage. Enterprises are increasingly recognizing the competitive advantage of applying AI insights to business objectives and are making it a businesswide priority.
- 20. Will an AI steal your job? • The possibility of artificially intelligent systems replacing a considerable chunk of modern labor is a credible near-future possibility. • Artificial intelligence won't replace all jobs, what seems to be certain is that AI will change the nature of work, with the only question being how rapidly and how profoundly automation will alter the workplace. • However, artificial intelligence can't run on its own, and while many jobs with routine, repetitive data work might be automated, workers in other jobs can use tools like generative AI to become more productive and efficient. • There's a broad range of opinions among AI experts about how quickly artificially intelligent systems will surpass human capabilities. • Fully autonomous self-driving vehicles aren't a reality yet but, by some predictions, the self- driving trucking industry alone is poised to take over 500,000 jobs in the US inevitably, even without considering the impact on couriers and taxi drivers.
- 21. Challenges and Limitations of AI While AI is certainly viewed as an important and quickly evolving asset, this emerging field comes with its share of downsides. AI is a boon for improving productivity and efficiency while at the same time reducing the potential for human error. But there are also some disadvantages, like development costs and the possibility for automated machines to replace human jobs. It’s worth noting, however, that the artificial intelligence industry stands to create jobs, too — some of which have not even been invented yet. Artificial Intelligence Benefits AI has many uses like • Boosting vaccine development • Automating detection of potential fraud. • Safer Banking • Better Medicine • Innovative Media
- 22. Future of Artificial Intelligence • When one considers the computational costs and the technical data infrastructure running behind artificial intelligence, actually executing on AI is a complex and costly business. Fortunately, there have been massive advancements in computing technology, as indicated by Moore’s Law, which states that the number of transistors on a microchip doubles about every two years while the cost of computers is halved. • Although many experts believe that Moore’s Law will likely come to an end sometime in the 2020s, this has had a major impact on modern AI techniques — without it, deep learning would be out of the question, financially speaking. Recent research found that AI innovation has actually outperformed Moore’s Law, doubling every six months or so as opposed to two years. • By that logic, the advancements artificial intelligence has made across a variety of industries have been major over the last several years. And the potential for an even greater impact over the next several decades seems all but inevitable.