You're considering starting a career in AI. What are the most important things you need to know?

A different way to impart intelligence in computers. Non-AI software is based on fixed rules (If this then do that). Programming is basically writing rules. AI uses historical (past) data to enable future decisions. An AI system "learns" from Training Data that correlates some sort of input with some sort of output decision. It's called training. It generalizes from the training data to new situations, by recognizing patterns in the data. E.g., you feed it annotated pictures with marked cats, and after enough pictures it can recognize cats; i.e., it can decide what is a cat and what is not (which can't be done with rules!). A well-trained AI generalizes just enough. Over or under generalizing create errors. It's NOT magic!

Einfach gesagt ist KI ein überbegriff für mathematische Methoden, welche menschliche (kognitive) F?higkeiten imitieren, dabei jedoch vom Menschen angesteuert werden. Dies kann von simplen Regressionen und Klassifikationen bis zu Generation natürlicher Sprache und kreativen Texten führen.

AI is the ability for computers to mimic human intelligence and behavior. Recent references to AI really mean Large Language Models (LLMs), which trained on massive amounts of data like the internet and learned to imitate and predict human language through machine learning.

Artificial Intelligence (AI) refers to the capability of machines to imitate human intelligence and perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, perception, and language understanding. In simpler terms, AI enables computers to be smart and make decisions without explicit programming. AI relies on various techniques, including machine learning, where computers learn from data patterns to improve their performance over time. Deep learning, a subset of machine learning, involves artificial neural networks that mimic the human brain's structure to process information and make decisions.

IA, ou Inteligência Artificial, refere-se à capacidade de sistemas computacionais executarem tarefas que normalmente exigiriam inteligência humana. Essas tarefas incluem aprendizado, raciocínio, resolu??o de problemas, reconhecimento de padr?es, processamento de linguagem natural e tomada de decis?es. A IA pode ser classificada em duas categorias principais: IA fraca (ou estreita), que é projetada para realizar tarefas específicas, e IA forte, que teria a capacidade de realizar qualquer tarefa intelectual humana. Algoritmos, redes neurais e aprendizado de máquina s?o componentes essenciais da IA, permitindo que os sistemas aprendam e melhorem com a experiência.

Neben dem IKT Sektor setzt besonders die Finanzbranche seit mehreren Jahren auf unterschiedlichste KI Methoden. Anwendungsf?lle reichen hier vom Credit Scoring, über Konversationelle KI im Kundenservice und für Mitarbeiterproduktivit?t, bis zur Nutzung von Anomaliedetektion zur Analyse von betrügerischen Aktivit?ten.

Anything worth automating that cannot be expressed in logical rules is a candidate application. Try to write ???? ???????? ???????? ???? ???????? rules to a system that KNOWS NOTHING to enable it to recommend movies, recognize cats, or understand English. You will find you can't. There are things that cannot be decided by logical rules. Instead of being rule-driven like non-AI software, AI is data-driven, just as humans are experience-driven. What are the applications of human intelligence? Anything, really. Forget about the A in AI. It's an intelligence, and the applications of intelligence are limitless. The limitations of AI are the limitations of the algorithms we have, and those are quickly improving.

A few applications worth mentioning are# Healthcare: AI aids in diagnostics, personalized medicine, and treatment planning. It can analyze medical data, predict disease outcomes, and assist in drug discovery, enhancing overall healthcare efficiency. Business and Finance: AI optimizes business operations through automation, data analysis, and decision support. In finance, it aids in fraud detection, algorithmic trading, and risk management, providing valuable insights for better decision-making. Education: AI facilitates personalized learning experiences, adapting to individual student needs. It can automate administrative tasks, offer virtual tutoring, and provide educational content, making learning more accessible and effective.

AI can be leveraged upon by most industries to aid Humans. From the Healthcare , Marine Biology to Chatbots ,Facial Recognition, Fraud detection ,Banking ,Entertainment & Education to Supply chain ,AI can be used ethically for the betterment of society .

A Inteligência Artificial (IA) tem amplas aplica??es em diversas áreas. Na saúde, auxilia em diagnósticos e tratamentos personalizados. No setor financeiro, contribui para previs?o de mercado e detec??o de fraudes. Na educa??o, proporciona sistemas de tutoria inteligente e avalia??o automatizada. No varejo, destaca-se em recomenda??es personalizadas e gest?o de estoques. No setor automotivo, impulsiona o desenvolvimento de veículos aut?nomos e manuten??o preditiva. Na tecnologia da informa??o, auxilia em gerenciamento de redes e seguran?a. Em recursos humanos, facilita o recrutamento e a avalia??o de desempenho.

Habilidades essenciais para trabalhar em Inteligência Artificial incluem programa??o (Python, Java, C++), conhecimento em aprendizado de máquina, ciência de dados, matemática e estatística, vis?o computacional, processamento de linguagem natural, raciocínio lógico, redes neurais, ética em IA, resolu??o de problemas, adaptabilidade e compreens?o de negócios. Manter-se atualizado com as tendências é crucial devido à natureza dinamica do campo.

To excel in AI, individuals should possess a combination of technical and soft skills. Technical proficiency in programming languages like Python and understanding of machine learning frameworks are crucial. Mathematics and statistics knowledge aids in algorithm development and data analysis. Problem-solving skills are essential to address complex challenges, and creativity helps in devising innovative solutions. Additionally, a strong foundation in computer science concepts, including algorithms and data structures, is beneficial. Adaptability and a curiosity-driven mindset enable individuals to stay abreast of advancements.

Python is currently the most used programming language for AI. Basically, your AI application will be only as powerful as the underlying model is. As we speak, ML models available in Python are advanced and are continuously updated. So learning Python driven ML modeling can take one a long way.

Reliability - While Im oversimplifying, AI is like a child's brain. It only learns what we teach and assumes it is the whole truth. So if underlying data is misguided or inaccurate, AI will behave like that as well. Evaluation - AI Performance evaluation is not very statistical, and not completely evolved. Evaluating AI application is a critical foundation for improving the application and utility further. Today, it requires a lot of manual efforts (which is expensive) and hence companies that apply AI arent able to get the full benefit from AI. Other Challenges - Data Privacy Management, Hallucination, Ethical consideration, and computing costs.

Für über 80% der Befragten in einer Studie des IBM Institute for Business Value, waren 1 der 4 folgenden Faktoren ein signifikantes Hindernis für die Nutzung von KI im Unternehmen. 1. Ethik; 2. Fairness; 3. Bias; 4. Erkl?rbarkeit. Mit einer gut aufgesetzten AI Governance mit Risikomanagement, Dokumentation, Monitoring und Erkl?rbarkeit, k?nnen diese Hindernisse gut überwunden werden.

Generative AI is based on the data quality and availability ,so there can be a very good probability of the responses to be not accurate and AI doesn't have the Empathy and Intelligence of Human Beings. Deepfakes are a very disturbing trend. There can be conscious/unconscious bias in how AI is used .AI is to be used as a collaborator and facilitator for Humans.

Ethical Concerns: AI raises ethical dilemmas like bias in algorithms, privacy issues, and potential misuse. Addressing these concerns is crucial to ensure responsible and fair AI deployment. Lack of Transparency: The inner workings of complex AI models are not visible, leading to a lack of understanding about decision-making processes. So enhancing transparency is essential for trust and accountability. Job Displacement: Automation through AI has the potential to replace certain jobs, leading to unemployment and socioeconomic challenges. Preparing the workforce for AI-related changes is vital to mitigate negative impacts on employment.

Os desafios da Inteligência Artificial (IA) incluem viés e ética, interpretabilidade dos modelos, privacidade e seguran?a, quest?es de sustentabilidade, impacto no emprego, compreens?o do contexto, falta de regulamenta??o, aprendizado com dados limitados, busca por inteligência artificial generalizada, aceita??o social e a necessidade de colabora??o internacional para regulamenta??o e desenvolvimento ético. Abordar esses desafios requer esfor?os contínuos de diversos setores para garantir um uso responsável e benefícios generalizados da IA.

Humans can reach very different conclusions even with the same information. Why? Because they are experience-driven, not rule driven, and everybody's experience is different. The truth is, humans are opaque black boxes. Even YOU do not know why you have specific opinions. AI is similar in that it's also data-driven. So it is similarly opaque. The ugly truth is nobody can predict an AI with complex training data. So,the ethics of AI mirror the complexity of the ethics governing our intelligence. The most effective way to shape a human is how to bring them up & what to show them, esp as kids. Similarly, the ONLY real AI lever is selecting its training data. So, many of the ethics problems boil down to problems in its training data.

1. Ensuring transparency in AI systems to understand how they make decisions and operate. 2. Addressing biases in AI algorithms to prevent discriminatory outcomes and ensuring fairness in the treatment of diverse individuals. 3. Respecting privacy by safeguarding sensitive data and being transparent about how data is collected, used, and stored. 4. Establishing accountability for AI systems, including clear lines of responsibility for their actions and outcomes. 5. Prioritizing the security of AI systems to prevent misuse or unauthorized access, protecting against potential threats. 6. Considering the broader social impact of AI applications, including potential job displacement, economic inequality, and other societal consequences.

Die Ethik von KI ist ab dem Jahr 2015 mehr in den Fokus der Tech Firmen gelangt. Bücher wie weapons of mathdestruction, aber auch ?ltere Filme wie Terminator haben sich mit dem Thema befasst. Ereignisse wie Black Lives Matter haben dazu geführt, dass gro?e Tech Firmen wie IBM und Microsoft bspw. die Nutzung von Facial Recognition für überwachungszwecke stark eingeschr?nkt haben. Ethische KI f?ngt bei Prinzipien an, inkludiert Regelen, Prozesse und das durchgehende Monitoring und Hinterfragen der eigenen KI und des Handelns.

Fairness and Bias: Ensuring fairness in AI systems involves addressing biases in data and algorithms to prevent discrimination. Ethical AI development requires promoting equal opportunities and minimizing unjust impacts. Transparency and Accountability: Developers should strive for transparency in AI decision-making processes to build user trust. Establishing accountability for AI outcomes, especially in critical applications like healthcare and criminal justice, is crucial for responsible deployment. Privacy Protection: Respecting user privacy is fundamental. AI systems must handle sensitive data responsibly, with robust measures in place to safeguard individuals' personal information and prevent unauthorized access or misuse.

Embarking on a non-technical AI career, such as GTM, necessitates foundational knowledge in data analytics, with an emphasis on current tools and technologies. Diving deeper into data science offers advantages too. Understanding AI's definitions, applications, challenges, and ethics is vital. Equally crucial are communication skills for explaining complex AI concepts in accessible language, essential across roles from design to marketing. This skill set ensures effectiveness and adaptability in AI's dynamic landscape, especially for those in non-technical positions.

Based on my experience if you are in college consider doing a minor in economics and take fundamental statistics these would really help you work, analyze and present data also help you make decisions. The most important aspect is looking at in different perspectives. Train oneself on interpreting and presenting the results. Understand how different ML algorithms work and statistics behind it. Work on used cases which would benefit from AI and ML specific to a business domain.

Embarking on a career in AI necessitates a deep understanding of machine learning and deep learning concepts, alongside proficient programming skills in Python and familiarity with key libraries like TensorFlow and PyTorch. Mastery of mathematics, statistics, and data handling techniques is essential, as is the ability to evaluate and interpret models effectively. Domain-specific knowledge and ethical considerations are paramount, requiring continuous learning to stay abreast of rapid advancements. Additionally, honing problem-solving, collaboration, and communication skills are vital for navigating multidisciplinary projects and contributing meaningfully to AI's evolution and societal applications.

Sharing my bit of what has shaped my AI journey: * Sharp decision-making skills - as an AI practitioner, you must be adept in developing the rationale that would help you make the decisions at speed. * Inquisitive - asking the why's. I had asked a lot of why questions - Why is it done this way? Why AI? Why now? * Collaboration - no successful AI initiative is ever done in silos. Network, talk to teams, get their views, brainstorm, be able to communicate to different forums. * Feedback - Similar to how models evolve and become better with feedback; this is our success recipe too. Keep soliciting feedback as much as possible and learn to iterate. In no time, you'll become an expert by being a better version of yourself everyday.

Always be Learning. This field moves quickly, alot of it may not be appliable to your specific role of use case, but any learning opportunity is a net positive for a future day. A new AI approach can inspire you to develop innovative solutions or change your perspective to resolve an issue.