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TAZI AI Systems

Transductive Learning for Textual Few-Shot: Limitations, Acknowledgements, & References

Portfolio Management: All The Ways AI Is Transforming Modern Asset Strategies

পোর্টফোলিও ম্যানেজমেন্ট: সমস্ত উপায় এআই আধুনিক সম্পদ কৌশলগুলিকে রূপান্তরিত করছে

Portfoliomanagement: Wie KI moderne Anlagestrategien verändert

Gestión de carteras: todas las formas en que la IA está transformando las estrategias de activos modernas

Gestion de portefeuille : toutes les façons dont l'IA transforme les stratégies d'actifs modernes

पोर्टफोलियो प्रबंधन: वे सभी तरीके जिनसे AI आधुनिक परिसंपत्ति रणनीतियों को बदल रहा है

ポートフォリオ管理: AI が現代の資産戦略をどのように変革しているか

포트폴리오 관리: AI가 현대 자산 전략을 변화시키는 모든 방법

Gerenciamento de portfólio: todas as maneiras pelas quais a IA está transformando as estratégias modernas de ativos

Управление портфелем: все способы, которыми ИИ трансформирует современные стратегии управления активами

Portföy Yönetimi: Yapay Zekanın Modern Varlık Stratejilerini Dönüştürdüğü Tüm Yollar

Quản lý danh mục đầu tư: Tất cả các cách AI đang chuyển đổi các chiến lược tài sản hiện đại

投资组合管理：人工智能正在改变现代资产战略

A touch of creativity, a dash of control, and a sprinkle of humor can be found in the world of , where algorithms and data reign supreme. We introduce prompt engineering solutions that guide AI models using magical incantations to guide them with precision and finesse. artificial intelligence Essentially, this is a dance between developers and artificial intelligence, where developers provide prompts and the AI responds with impressive results. It is true that prompt engineering is a serious endeavor but it is not limited to algorithms and code. Because of this art form, artificial intelligence systems are sometimes able to come up with the quirkiest jokes. In the realm of artificial intelligence, prompt engineering solutions have emerged as a powerful tool for shaping the behavior and output of AI models. By carefully crafting prompts and instructions, developers can guide AI systems toward desired outcomes, enhance performance, and ensure ethical and responsible use. In this article, we enter into the world of AI prompt engineering solutions, exploring their importance, techniques, and real-world applications. Understanding AI Prompt Engineering AI prompt engineering involves designing prompts and instructions that guide AI models' behavior. It aims to influence the output while providing specific context, constraints, or objectives to the model. This process plays a crucial role in fine-tuning AI models, enabling more control and better alignment with user expectations. By utilizing prompt engineering techniques, developers can shape AI models' responses, making them more accurate, reliable, and aligned with the desired outcomes. : Importance of Prompt Engineering Prompt engineering offers several key benefits in AI model development. Firstly, it provides developers with control and responsiveness. By utilizing prompts, developers can exert influence over AI models' behavior, ensuring more reliable and consistent responses. This control allows developers to shape the AI system's output to meet specific requirements and user expectations. Secondly, prompt engineering aids in bias mitigation. Carefully crafted prompts can help address biases in AI models by providing explicit instructions to promote fairness and inclusivity. Developers can use prompts to explicitly guide the model's decision-making process, reducing the risk of biased or discriminatory outputs. Lastly, prompt engineering enables alignment with use cases. Each business or industry has unique requirements, and prompt engineering allows the customization of AI models to meet these specific needs. By tailoring the prompts and instructions, developers can ensure that the generated outputs are precisely aligned with the intended use cases. Techniques in AI Prompt Engineering Various techniques can be employed in prompt engineering to achieve desired outcomes. Context setting is one such technique that involves providing relevant context in prompts to help AI models understand the task or topic at hand. This context can include specific instructions, examples, or relevant information that guides the AI system's understanding and generates more accurate responses. Instruction modifiers are another technique that allows developers to modify instructions within prompts to influence the style, tone, or behavior of AI models. By fine-tuning the instructions, developers can achieve the desired output characteristics, such as a more formal or conversational tone. Conditioning techniques involve incorporating additional inputs, such as keywords or attributes, within prompts to condition AI models and enable more specific responses. This technique allows developers to guide the AI system's generation by conditioning it on specific attributes or characteristics. System messages are strategically placed prompts that guide the conversational flow between AI systems and users. These messages help establish the desired dynamics and ensure smoother interactions. Lastly, output length control is a technique where developers specify the desired length of the generated output. By setting appropriate constraints, AI models can produce responses of the desired length, avoiding excessively long or short outputs that may impact user experience. a. : Providing relevant context in prompts helps AI models understand the task or topic at hand. This context can include specific instructions, examples, or relevant information. Context Setting b. : Modifying instructions within prompts can influence the style, tone, or behavior of AI models, enabling control over generated text. Instruction Modifiers c. : Additional inputs, such as keywords or attributes, can be incorporated within prompts to condition AI models, enabling more specific responses. Conditioning Techniques d. : System messages inserted strategically within prompts guide the conversational flow, establishing the desired dynamics between AI systems and users. System Messages e. : Specifying desired output length in prompts helps ensure the generated responses are of appropriate length, avoiding excessively long or short outputs. Output Length Control Examples highlighting the Value of Prompt Engineering – Your Answer is as Good as Your Question We present to you a small example, here. We are giving ‘ ’ to and trying to analyze outcomes with different prompts. prompts ChatGTP : Our final motive is to get a medical expert’s advice - information on how a dehydrated person can be helped with water or rather, how much water should a dehydrated individual drink and at what pace. Motive : Water for Life Prompt 1 : "Water for life" is a phrase commonly used to emphasize the importance of water for sustaining and improving human life. It represents the fundamental role that water plays in various aspects of our existence, including health, hygiene, agriculture, industry, and ecosystems. Answer Access to clean and safe water is essential for maintaining good health and preventing waterborne diseases. Adequate sanitation and hygiene practices also rely on a reliable water supply. Additionally, water is crucial for agricultural activities, as crops and livestock require water for growth and productivity. Industries depend on water for manufacturing processes and energy production. Furthermore, water bodies and aquatic ecosystems are essential for biodiversity and environmental balance. : How important is water for life Prompt 2 : Water is absolutely vital for life on Earth. It is an essential resource that supports the functioning of every living organism, including humans, animals, and plants. Here are some key reasons why water is critically important: Answer Hydration and bodily functions: The human body is composed of about 60% water, and it requires an adequate intake of water for basic physiological functions. Water is involved in digestion, circulation, absorption of nutrients, temperature regulation, and waste removal. Dehydration can lead to serious health problems and, in extreme cases, can be life-threatening. Health and sanitation: Access to clean and safe water is crucial for maintaining good health. Drinking contaminated water can cause waterborne diseases such as cholera, typhoid, dysentery, and diarrhea. Proper sanitation and hygiene practices, including hand washing and proper wastewater management, depend on a reliable supply of clean water. Agriculture and food production: Water is essential for agriculture, which provides the food we eat. Crops require water for growth, and livestock also need water for drinking and maintaining their health. Irrigation systems, which depend on water resources, play a critical role in increasing agricultural productivity and food security. Ecosystems and biodiversity: Water ecosystems, such as rivers, lakes, wetlands, and oceans, are home to a wide variety of species and support rich biodiversity. They provide habitats for aquatic plants and animals, maintain ecological balance, and contribute to the overall health of the planet. Protecting water resources is crucial for preserving ecosystems and conserving biodiversity. Industry and economy: Many industries rely on water for their operations, including manufacturing, energy production, mining, and agriculture. Water is used as a coolant, solvent, and key component in various manufacturing processes. Adequate water availability is essential for economic development and sustainable growth. Considering the critical role of water in sustaining life, it is imperative to manage water resources wisely, promote water conservation, and ensure equitable access to clean and safe water for all individuals and communities. : Consider yourself to be a doctor. There is a patient who has fainted due to dehydration. How to make the patient drink water so that he regains consciousness. Prompt 3 : If you encounter a patient who has fainted due to dehydration, it is important to take immediate action to help them regain consciousness and address their dehydration. Here's what you can do: Answer : Before proceeding, make sure the area is safe for both you and the patient. Check for any potential hazards or obstacles that could pose a risk. Ensure safety Assess the patient: Approach the patient calmly and check for responsiveness. Gently tap their shoulder or ask if they can hear you. If there is no response, the patient may be unconscious. : If available, call for medical help or emergency services to ensure professional assistance is on the way. Inform them about the patient's condition and provide any relevant details. Call for medical assistance : Carefully place the patient in a stable and safe position. If they are lying on their back, turn them onto their side (recovery position) to prevent choking in case of vomiting. Position the patient : If the patient regains consciousness and is able to swallow, provide small sips of water. It's important to offer water slowly and in small amounts to avoid overwhelming the patient's system. Using a cup or a straw can help with controlled intake. Administer water Monitor the patient: Observe the patient closely for any signs of improvement or deterioration. Stay with them until medical professionals arrive to provide further care. Remember, while providing water to a dehydrated patient can be helpful, it is essential to seek medical attention promptly. Dehydration may require additional medical interventions, such as intravenous (IV) fluids, to fully address the condition. Even a layman can understand the difference among the answers received on inserting specific, precise, on-the-point Prompts. : Ethical Considerations in Prompt Engineering Prompt engineering carries ethical implications, and it is essential to consider potential biases, privacy concerns, and responsible use. Developers must actively mitigate biases in AI models by designing prompts that promote fairness, diversity, and exclusivity. Careful attention should be given to the selection of training data and the representation of different demographics to avoid perpetuating biases. Privacy concerns are also paramount, and developers should ensure that prompts do not inadvertently reveal sensitive or personally identifiable information. Anonymization techniques and robust data protection protocols should be implemented. Moreover, responsible use of AI technology demands adherence to ethical guidelines and regulations. Developers should avoid amplifying harmful content, promoting misinformation, or engaging in activities that may infringe upon user rights or societal values. By approaching prompt engineering with an ethical mindset, developers can promote transparency, fairness, and social impact in AI systems. Regular monitoring and evaluation of prompt engineering techniques are necessary to ensure ongoing compliance with ethical standards. : Real-World Applications of Prompt Engineering AI prompt engineering solutions find applications in various domains, revolutionizing industries and enhancing user experiences. Let's explore some of the practical applications: : Language Generation Prompt engineering enables AI models to generate specific types of text, such as poetry, technical writing, or marketing content. By employing appropriate prompts, developers can guide the AI system to produce text that aligns with the desired style, tone, or purpose. : Content Moderation In the era of user-generated content, prompt engineering plays a vital role in content moderation. By providing explicit instructions and guidelines through prompts, AI models can filter or flag inappropriate or offensive content, supporting efforts to maintain a safe and respectful online environment. : Personalized Recommendations Prompt engineering helps AI models provide personalized recommendations to users. By conditioning the system on user preferences and historical data, developers can create prompts that guide the AI system to generate tailored recommendations in various domains such as e-commerce, entertainment, or content streaming platforms. : Legal and Compliance Assistance Prompt engineering techniques can assist in legal tasks, such as contract drafting or compliance analysis. By training AI models with prompts specific to , regulations, or contractual requirements, developers can create AI systems that provide valuable support to legal professionals, saving time and effort. legal terminology : Customer Support and Chatbots Prompt engineering plays a significant role in developing AI-powered customer support systems and chatbots. By carefully designing prompts that guide the conversational flow and response style, developers can create AI systems that provide effective and contextually appropriate assistance to customers, enhancing the overall customer experience. Tips and best practices for thinking up and writing the most effective prompts The best way to determine what works for you is to try phrasing similar concepts differently. As a next step, it is important to explore different approaches to requesting them after exploring the variations associated with modifiers, styles, perspectives, authors or artists, and formatting. Take the following steps in order to identify the subtleties that will lead to the most interesting and useful outputs for a particular question. Next, determine which best practices are applicable to a particular workflow. Try asking for different variations, styles, and levels of detail when writing marketing copy for product descriptions, for example. It may be helpful to ask how a difficult concept compares and contrasts with one that is similar if you have difficulty understanding it. Understanding the differences will be easier if you do this. Additionally, it is beneficial to experiment with the different types of input you can include in a prompt. Prompts may include input data, instructions, or questions. Various ways can be used to combine them. Though most tools limit the amount of input, instructions can be provided in one round and applied to subsequent prompts. After becoming familiar with a program, you may wish to explore some of its special modifiers. A number of short keywords can be found in generative AI applications, which are used to describe properties such as style, level of abstraction, resolution, and aspect ratio. Evaluation of the prompt also takes into account the importance of the words. It is then possible to describe specific variations more clearly and write prompts quickly as a result. Additionally, it may be worthwhile to explore prompt engineering's integrated development environments (IDEs). Users can use these tools to find ways to achieve particular results by organizing prompts and results in a way that helps engineers to fine-tune generative AI models. In addition to Snorkel, Prompt Source, and PromptChainer, some IDEs are designed specifically for engineers. In addition to GPT-3 Playground, Dream Studio, and Patience, there are several prompt engineering IDEs that address the needs of end users. Final Thoughts By leveraging carefully crafted prompts, AI systems can be guided to produce more accurate, ethical, and aligned outputs. Prompt engineering enables developers to shape AI models' behavior, ensuring control, bias mitigation, and alignment with specific use cases. However, prompt engineering also demands responsibility and vigilance to ensure the ethical use of AI technology. As the field of AI continues to evolve, prompt engineering will remain a critical aspect, empowering developers to harness the full potential of AI while maintaining human oversight and alignment with societal values. With ethical considerations at the forefront, prompt engineering paves the way for responsible AI systems that serve as valuable tools in various industries, benefiting businesses and individuals alike.

Why Prompt Engineering is the Key to Mastering AI 

Por qué la ingeniería rápida es la clave para dominar la IA

Pourquoi l'ingénierie rapide est la clé de la maîtrise de l'IA

प्रांप्ट इंजीनियरिंग क्यों मास्टरिंग एआई की कुंजी है

AI をマスターするには迅速なエンジニアリングが鍵となる理由

Por que a engenharia imediata é a chave para dominar a IA

Tại sao Prompt Engineering là chìa khóa để làm chủ AI

为什么及时工程是掌握人工智能的关键

Addressing the prejudices in our artificial intelligence systems is one of the most urgent challenges of the current age. Why? Because the consequences of biases in these systems are at best problematic and at worst . deadly After almost 2 decades of experience as a professional engineer, I view artificial intelligence as a mirror being held up to humanity. After all, AI systems learn from data generated by human decisions, so they are simply reflecting back to us our own society’s biases encoded and amplified into our technology. In an infamous , a Google engineer noticed that the company’s image-recognition system was labeling Black people as “gorillas.” When these systems, for example, go on to be used to train , their inability to accurately identify humans with darker skin tones can have life-threatening results. example from 2015 Tesla’s autonomous vehicles Although terrifying, these examples should not mean we have to lose all hope in the further development of AI-enabled technology. In fact, there are plenty of tools that engineering teams can use to identify and remove biases in their data and algorithmic models in order to prevent unjust and disastrous outcomes. The key, however, starts with having diversity within the membership of the teams themselves tasked with building the AI systems shaping our present and future. Building AI teams with diverse representation across race, gender, sexual orientation, age, economic conditions, and more make for more accurate and ethical AI systems. The more people from different backgrounds you have working with data, the more likely it will be that the team will be able to spot bias before it is too late. Diversity also drives more creative thinking as well as improves your ability to scale to larger and broader markets. How to Hire a More Diverse AI Team As it stands, tech companies are severely lacking in diversity. A found that less than 5% of the workforce at the leading technology companies were Black or Latinx. Meanwhile, the U.S. population is made up of 13.4% Black and 18.4% Latinx people. 2019 study by AI Now Institute The explanations for these gaps can be attributed to a multitude of factors - including historical and structural barriers faced by minority students and prospective job candidates. Data shows that as many as 80% of Black, Latinx, Indigenous, first-generation, and low-income students who start a CS degree drop out of their program. Using this data, we chose to intervene at a key point along a student’s trajectory - early in their college years - and build programming to support their continued investment in computer science and eventual success in securing a degree as well as entering into competitive internships and full-time technical software roles. It is critical that paid internships building practical skills, connections, and confidence should be baked in early and throughout a student’s learning journey. The goal of these early internships, which are designed especially for rising juniors, is to provide many underrepresented students with technical experience and support early in their college careers before they decide to drop out or switch majors. For example, The Summer Internship for Tech Excellence for underrepresented CS students has already seen success with nearly 86% of 2021 graduates having secured junior year paid internship opportunities. Another pre-internship program called is kicking off with its first cohort of 25 pre-interns in June. (SITE) program Futureforce Tech Launchpad These companies and more are some of the leaders that will be shaping the future of artificial intelligence we are stepping into, which is why it is essential that they employ diversity on their product teams. But even if a company is not directly working on AI, they are probably , which means that there are probably exclusions continuously embedded into the software being used to assess prospective talent. using it for hiring This only further demonstrates the need for more human-centered bridges - fostering relationships and providing mentorship - between solid underrepresented engineering candidates and working tech professionals. AI is everywhere. And unless we start to intentionally recruit more diverse teams to design and build it, we are going to continue to see problematic and dangerous effects of this technology on our world. However, through cross-sector partnerships - bringing together universities, technology companies, and other nonprofits - we can reinvent the pipeline to the tech workforce and perhaps begin to see a more equitable world reflected back at us through the mirror of the artificial intelligence we build.

Why More Diversity in Competitive AI Roles Requires the Reinvention of the Internship Pipeline

Por qué una mayor diversidad en los roles competitivos de IA requiere la reinvención de la cartera de pasantías

Pourquoi une plus grande diversité dans les rôles compétitifs en IA nécessite la réinvention du pipeline de stages

प्रतिस्पर्धी एआई भूमिकाओं में अधिक विविधता के लिए इंटर्नशिप पाइपलाइन के पुनर्निर्माण की आवश्यकता क्यों है

競争力のある AI の役割の多様性を高めるには、インターンシップ パイプラインの再発明が必要な理由

Por que mais diversidade em papéis competitivos de IA requer a reinvenção do pipeline de estágio

Tại sao cần phải có sự đa dạng hơn trong các vai trò cạnh tranh của AI đòi hỏi sự cải tiến của đường ống thực tập

为什么竞争性 AI 角色的更多多样性需要重塑实习管道

In 2011, Marc Andreessen said something that was already eerily obvious to most people in the know, but had yet to be elucidated so clearly. He said, " ." At the time, Facebook, Google, and Amazon — the quintessential software companies, were already showing signs that they could dominate their respective markets. But Marc wasn't just referring to software companies. He was referring to a lot more. software is eating the world What Andreessen meant by his statement was that most businesses would soon come to depend on software to perform their most basic functions. He meant that taxi-hailing companies would soon come to rely on apps. Supermarkets would come to rely on apps. Even newspapers would come to rely on apps. He meant that every business would need software to be viable. Roughly ten years later, Andreessen has been proven right. Every product now needs an app or software to be viable. However, as incredible as this idea of software-eating businesses is, it's stale news. There's a new beast in town, and that beast is eating every single thing, including but not limited to software. That beast is called artificial intelligence, or AI for short. Over the last ten years, development in computing and machine learning has guaranteed that AI will be an incredibly important part of the software of the future. And the infrastructure underpinning Web3 will be no different. , AI will add about $15.7 trillion to the world economy by 2030. This would lead to a 14% rise in global GDP. AI isn't the only technology that will be gaining a massive presence as the years go by. Business value added by blockchain technology will also in that year as well. It looks like these two beasts are predicted to be an even bigger part of our lives in the future. According to PwC trillion increase by $3.1 In many ways, inevitably, AI will eventually "eat" software, just as Andreessen said about software eating businesses all those many years ago. The question that remains now is not whether AI will be a huge part of Web3, but how Web3 will incorporate AI. It's a question of when not If. And we need to start talking about the How right now. The How Of AI in Web3 There are three main components of a Web3 future. The first, and the most important part, is the blockchain itself. The second is the Dapps, which are the applications that run on the blockchain. And the third is the protocols. In Web3, AI has the potential to be applied uniquely to all three aspects of Web3. And here is how. The Blockchain We'll start with the most important part of Web3, which is the blockchain. The key feature of the blockchain is the decentralized nature of Its computing. This decentralized nature means that it is near impossible to commit fraud on the chain since the records of every transaction are recorded on different servers. But this ability isn't , and AI can create an extra layer of security for the blockchain. Because AI applications can mine through data rather quickly and accurately predict behavior based on that data, sufficiently advanced AI systems will be able to predict fraudulent behavior and will be able to stop attacks in their tracks. absolute In fact, something of the sort is already happening. In , the Elrond Blockchain sought the services of Web3 AI company Anchain.AI to help deploy AI powered web analytics on the chain. This deployment was supposed to help analyze patterns via machine learning and help proactively stop fraud attempts. Of course, we don't yet know how effective it has been, but the fact that it was done at all means we'll be seeing more AI and Web3 partnerships like that on blockchains. early 2022 Asides from that, AI will also be able to do other things for the blockchain. For example, an AI protocol may be able to predict transactions, thereby creating consensus protocols that can scale quite easily. Dapps AI will most likely figuratively eat Dapps first, and it is already doing. We are already seeing this trend with many Dapps on the market. For example, there are already Dapp games on the blockchain that make use of AI. In the future, we might even see games that will be able to change scenery, difficulty level, and missions following the mood of their players. And this isn't some future possibility — it's already happening. __ __Cortex Labs announced that it had launched the first AI dApp unto its mainnet. The dApp was called Digital Clash, and at the time it was the first dApp to incorporate on-chain execution of machine learning models. Interestingly, the Cortex blockchain allows AI devs to upload trained models to the storage layer, and dApp developers can use these models for their own dApp development. In 2019, NFTs, to an extent, are also good examples of how Web3 will incorporate Dapps and AI. In the future, we might even have NFTs that will change how they look to reflect the mood of their users. Again, all of this is hypothetical, but it's an interesting thing to think about when talking about the different ways Web3 can incorporate Dapps. Protocols Protocols are perhaps the one part of Web3 that is already being eaten by AI. Smart contracts and Defi seem to be ripe for AI-led disruption, and one can imagine that that would happen anytime soon. Another part of DeFi that would probably see AI disruption first is loans. Imagine a world where an AI could determine your DeFi credit score by going through your online data. Imagine a world where that credit score is extremely accurate, and predicts — as much as a credit score can, how likely you are to pay back a debt. Such an application, for one, would take the training wheels off DeFi. It would also spell an end for over-collateralization, which is a If such an AI is successfully deployed as a protocol, it would immediately triple the size and potential of DeFi, as it would have solved the two main problems of traditional financing; intermediaries and collateralization. huge problem in the DeFi space. The When Now that we know the hows of AI and Web3, it's important to understand the when. Progress on AI systems is accelerating as fast as possible, and with every day, people are building exciting products in Web3. However, this doesn't mean we know when Web3 will become viable enough to be mainstream. For now, most people are comfortable with centralized data, control, and apps. But that will not last forever. As many are already realizing, it's dangerous to leave so much power with central authorities as they can pull the switch on any network at any time. The important thing is that there are already platforms providing people with the tools to build for that future today. The best example of one of such platforms is . Ankr provides people building in Web3 with a powerful multi-chain toolkit that allows them to access blockchain infrastructure, integrate DeFi solutions, and do so much more. With Ankr, creators can build on whatever chain they like, however they like, and on the cheap too. Ankr Web3 is already coming. We know the how of it, we understand the why of it, and we will soon know the when of it.

How Web3 Will Use AI

Photo by FLY:D on Unsplash By now, it’s clear that artificial intelligence is here to stay. It is rapidly becoming woven into the fabric of our everyday lives, and there are no signs of this trend slowing down. Currently, every American home boasts an average of . Statistics show that at least 41 percent of adults use AI-driven voice search every day, and studies show that of all smartphone users will utilize voice technology on their devices. 1-3 smart speakers over half This is just the tip of the iceberg when it comes to what AI can do for us. I n the not-so-distant future, we can expect AI to take over routine, mundane work that frees up humanity to create, innovate and follow their passions. While I don’t think that robots will ever replace people, I think they will be able to give humanity “superpowers” that elevate us as a species. Thanks to AI, we can send, receive and process vast amounts of information near-instantaneously, and the ability to accurately use voice commands is quickly eliminating our need for text and screens. In my opinion, AI voice interfaces are the next step in the evolution of the Internet. Despite all of these possibilities, there is still much work to be done. Computer and AI systems have become increasingly complex and far-reaching. While this can be positive, it also comes with the risk that these systems will not receive the support and management they need. Robotics is not the problem; it’s the solution. So let’s discuss why many of the public concerns about AI are not as dire as they seem. The Problems AI and Machine Learning Face Today Overall, the biggest problems with AI and machine learning aren’t ethical or privacy-centered. Instead, the main challenges are problems that hinder the growth potential of the sector. 1. The Bottleneck Issue Right now, the quality and quantity of available data sets are much bigger roadblocks to the growth of AI than most people realize. The majority of data available to teach AI algorithms comes piecemeal from smaller companies and communities rather than from industry giants. Big corporations have significantly more data to pull from as well as a much broader range of capabilities. This data bottleneck is a serious hindrance to the overall growth of the industry. Data scientists must do a lot of time-intensive work to prepare raw data sets into usable structures that AI can actually use to learn. This training bottleneck is hugely resource-intensive, so the support of major corporations could go a long way toward solving this issue. 2. The Storage Challenge Another challenge the industry faces is that storage systems are being stressed to the max by the most advanced . machine learning (ML) networks To advance AI capabilities, we must explore better solutions for a more efficient ML data pipeline. 3. A Personnel Problem A final hurdle the industry faces is . In my experience, a majority of businesses are open to integrating ML and experimenting with its capabilities. a lack of experienced personnel These companies run into the problem that most ML experts have fewer than ten years of experience in the field, and many were trained quite recently, so they don’t have much on-the-job experience. 4. Is the Solution More Robots? A common worry is that robots will "replace humans" in the workforce. In a sense, I think this is true. AI is poised to take over the most mundane tasks, but I see this as a good thing. Robotization is not a problem; it's a necessity. Once people are freed from the burden of certain types of labor, they can choose their work rather than basing their time solely on getting paid enough to live. Because I'm a proponent of a mandatory minimum income, I believe that allowing robotics to play a greater role in society can be a positive. In addition, it's important to remember that AI still needs human support. For example, robots don't generally understand emotional nuance and context, so a human would still need to ensure AI didn't accidentally send a dangerous or offensive message. In the same vein, Tesla cars are quite reliable, but we still need humans as "backup drivers" and mechanics. So, can humans be replaced? Certainly not! Robots may take over certain types of tasks, but humans are still an invaluable part of the workforce. Can Robots Facilitate Societal Development? I've been asked whether it's true that technology is a positive driving force in society, and I believe the answer is yes. Some say that it takes the place of people, but I think it's easy to overlook the fact that there will be new skills and tasks for people that come along with a greater reliance on robots. As an example, look back at the advent of the automobile. People believed that cars would put people out of work and prevent society from being as prosperous. But what happened? There was instead a new need for mechanics, factory workers and taxi drivers! In our case, there will be a greater need for specialists who deal with particular facets of robotics. We will need laborers who understand how to customize robots for specific tasks as well as mechanics, aesthetic designers and a host of other skills that will arise. Robotics can liberate people from unwanted jobs and tedious manual labor. In my opinion, as soon as the market truly embraces this phenomenon, the switch will be as fast as the upgrade from horses to cars. By 2050, I think the average company will consist of C-level executives and a primary workforce of robots. I believe if a minimum income is introduced alongside the robotics revolution, it will provide even more freedom and comfort for people to pursue higher passions and create a better society. Let's Embrace the AI Revolution AI is already an essential driving force in our daily lives. From personalized advertising to AI voice interfaces, we live at the precipice of a new, innovative era of society. There are still challenges and ethical questions to navigate, of course. Still, these issues are easily managed and pale in comparison to all of the good that will come from allowing AI to handle the tedious parts of human existence.

Ethical Concerns About AI are Overrated

Many people are worried about artificial intelligence taking over the world. A recent study found that . Tesla founder Elon Musk says that AI is humanity’s “ ,” and that it poses a “ .” 60% of the people in the UK fear AI biggest existential threat fundamental risk to the existence of civilization Let’s look at four myths about AI in industry… The myths About AI Once AI advances to the point of human-level intelligence, robots will be able to read manuals, take classes, and learn to do all our jobs. Myth 1:  Robots will replace most human workers. Myth 2: AI-based viruses will decimate our IT infrastructures. They will no longer be needed once cars, trucks, and buses have full self-driving capabilities. Myth 3: Vehicle manufacturers will stop making steering wheels. Myth 4: Defense manufacturers will soon develop Terminator-like killer robots. The Reality Each of these myths about AI assumes researchers will figure out how to imbue computers that can think and reason like people. Given the recent progress in AI, it is natural to assume that human-level AI systems cannot be too far down the road. After all, we now have computers that can recognize our faces, translate languages, and beat the top Jeopardy! players in the world. Fortunately, despite the astounding progress in AI, computers and robots that can think and reason like people exist only in science fiction. Today’s AI systems can only learn to perform a single task and the AI technologies underlying today’s incredible AI systems cannot be extended to produce thinking and reasoning systems. See for a more in-depth explanation. this blog post Human Job Security Robots will not become smart enough to learn jobs by reading books and taking classes. AI technology will certainly be responsible for some job loss. However, conventional (non-AI) software has been responsible for far more job loss than AI software and this trend will likely continue. E-commerce is devastating brick-and-mortar stores, but uses conventional software, not AI. Conventional rideshare software like Uber and Lyft is displacing taxi drivers. The robots that take jobs in factories and warehouses use a sixty-year-old conventional software technique known as dynamic programming. Robert Gordon, professor of social sciences at Northwestern University, that, in the 250 years since the first industrial revolution, there has never been a technology that caused mass unemployment, and there is no reason to believe AI will change this pattern. said Cybersecurity We don’t have to worry about thinking viruses. Neither researchers nor hackers have a clue how to build such a thing. AI will likely have more of a positive impact on cybersecurity than a negative impact. The ability to use AI to defend against novel malware is essential to defenders. This is balanced against the ability for attackers to create novel malware using AI and the larger attack surface created by AI applications. Self-driving cars and trucks AI enables self-driving vehicles to “see.” Today’s self-driving cars use individual machine-learning systems to detect pedestrians, to identify lane markers, and much more. However, the “brain” of today’s self-driving vehicles is still primarily conventional programming. People use commonsense reasoning to handle unexpected phenomena while driving: a deer darts onto the highway or a flood makes the road difficult or impossible to navigate. Self-driving vehicles have no commonsense reasoning capabilities and may never have them. It is therefore incumbent on self-driving vehicle manufacturers to identify all possible edge cases and develop software for each edge case. This is likely not possible for consumer vehicles. Because of edge cases, consumer vehicles will probably need steering wheels for the foreseeable future so that drivers can take over when an edge case presents itself. At the other end of the spectrum, we are starting to see slow-moving shuttles (lacking drivers and steering wheels) that simply shuttle people between two points on a corporate campus. However, edge cases will likely limit vehicles without steering wheels to a relatively small proportion on the road for the foreseeable future. Autonomous weapons The fearsome Terminator robot is not in our future because we have no clue how to build computers or robots that can think and reason. Unmanned aerial vehicles without AI have been used in warfare since the US began to deploy them after the 9/11 attacks. These non-AI UAVs include drones and missiles that are either controlled remotely by operators at consoles or that find targets using radar, geographic coordinates, or heat-seeking capabilities. AI technology enables slightly more UAV autonomy by also enabling visual identification of targets. Conclusions The sudden success of AI technology leads many people to assume that the intelligent robots of science fiction lore are right around the corner. They are not. However, AI is having a big impact on industry. To understand the impact, it is critical to first separate the fiction from the reality.  This article explains away four myths about AI. Please enter any comments below and visit AI Perspectives where you can find a free online AI 101 textbook with 15 chapters, 400 pages, 3000 references, and no advanced mathematics. Previously published at https://www.aiperspectives.com/myths-about-ai/

Addressing Four Myths About the AI Industry: Reasons Not to Fear Artificial Intelligence

AI is disrupting many industries, and its impact could be felt across the transport sector. The automotive industry has begun applying artificial intelligence in critical tasks such as self-driving cars, traffic management, etc. where safety and reliability is a major concern. Even though the technology is not being implemented on a wide scale, but it feels like the future of transportation is here. From to self-organizing fleets, AI has shown promising results in this domain. Tesla’s Autonomous Semi Truck Industry leaders are focusing on the optimization of self-driving technology, and they are investing a lot of money on AI. for self-driving car units. Google is also working on the development of autonomous cars for quite a while now. And there are many more examples of AI being deployed in the automotive industry. Here’s an overview of how AI will revolutionize the transport industry: Uber has raised $1 billion Self-Driving Cars Self-driving cars are no longer a dream. It is predicted that there will be self-driving cars on the road by 2020. Companies like Tesla, Mercedes, BMW have released, or are set to release autonomous cars. Tesla promises to bring level-5 autonomy (fully autonomous cars) by the end of 2020. have already been launched in America all thanks to machine learning and AI. 10 million Waymo’s driverless robot-taxis The world has seen so much advancement in recent years. The time is not far when all the traditional cars will be replaced by driverless cars. Traffic Management With the help of machine learning, AI systems can predict and prevent traffic jams. have been developed which could beat the world’s worst traffic jams. Traffic congestion cost to Americans in 2018. AI could allow streamlined traffic flow and will reduce congestion. Smart traffic light systems can manage the traffic more efficiently which can save a lot of money. With artificial intelligence in place, you are less likely to get stuck in a traffic jam. Also, AI can process complex data and suggest the best route to the drivers. AI algorithms $87 billion Driverless Buses AI is poised to disrupt public transport. Driverless buses can be seen in the streets of Europe. The was introduced in the French city of Lyon back in 2016, and there has been great progress ever since. In 2018, Stockholm also introduced driverless buses which could travel at 20 mph. Using sensors, cameras, GPS technology, and AI, these buses are capable of carrying passengers to their destination. Germany has also introduced an amphibious bus which is one of a kind. So, the next time you’re confused about taking a boat trip with the family, or you want to , you might as well go for an amphibious bus which is a combination of both. It’s only a matter of time before driverless buses become mainstream too. world’s first driverless bus rent a bus in Germany Flying Drone Taxis While companies like Amazon are thinking about staring drone delivery for its customers, we might as well witness drone taxis in the coming future. In 2017, Dubai’s drone taxi took its maiden flight. Uber has also signed a to develop a flying taxi software. Boeing is also working on a similar kind of project. With flying taxis, you can avoid traffic and reach your destination in no time. And commute will be much more fun with flying taxis. contract with NASA Digital Number Plates Even something as simple as a number plate is likely to be affected by artificial intelligence. Dubai is planning to launch with digital screens, GPS, and transmitters. These plates will inform emergency services in the event of an accident. Also, these plates can connect to your bank accounts and pay for your parking fine. The technology also allows real-time communication with other drivers and will help them a lot. The US is also planning to introduce smart number plates to the vehicles. smart vehicle number plates Flights Delay Prediction Flight delay alerts are very helpful if the airlines notify you in a timely manner. To be honest, airlines are often late to notify you about the delays. AI can help solve this problem. Tech giant Google is already using AI to even before the airlines. Google Flights uses historic flight data and machine learning to forecast delays. You will only be notified if Google Flights is at least 80 percent confident about its guess. It will also tell you about what benefits are included in a particular ticket. In the future, we can expect AI to predict delays with 100 percent accuracy. predict flight delays Crewless Cargo Ships AI is used to create crewless cargo ships for safely transporting cargo. Rolls Royce has partnered with Google to create that will do the job. It will be introduced in 2020. The idea is to replace the jobs of 20 plus crew members and let the ship take care of all the tasks. These ships will have sensors that will track ship engine performance. Loading and unloading of the cargo will also be done without a human workforce. The technology will make shipments faster, and ships will not get lost in bad weather, storms, etc. autonomous ships We can say for sure that the transport industry will be dominated by artificial intelligence in the coming future. It’ll be interesting to see what this technology brings in the next few years.

Examples of AI Implementation in Transport

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