Digital Skills: Artificial Intelligence by Accenture at FutureLearn

Digital Skills: Artificial Intelligence

Accenture

FutureLearn: Link

https://www.futurelearn.com/courses/artificial-intelligence/21/welcome

Artificial Intelligence (AI):

AI is designed to think and act like humans. There are two types of AI: weak AI (sometimes called narrow AI) and strong AI. Weak AI concentrates only on the task for which it was developed. Strong AI more closely approaches human intellect and is intended to do a variety of varied jobs. Strong AI aims to achieve intelligence levels comparable to humans. Strong AI, on the other hand, is still in its early stages and is not as useful or usable as narrow AI.

Origins and Progression of Artificial Intelligence:

Origins: 

AI traces its roots back to the 1950s when scientists began exploring ways to create machines capable of intelligent behavior.

Where did it come from?

Artificial intelligence is not new. It was initially established in 1956 by a group of scientists from the United States. Since then, artificial intelligence has gone through several cycles, with periods of significant scientific discoveries followed by 'AI winters' - periods of despair as AI failed to deliver on its promise. With the increasing usage of AI, responsible use, including ethics and prejudice, has become an important aspect of its development.

Early Progression: 

Initial AI efforts focused on tasks like problem-solving and symbolic reasoning.

Shift to Machine Learning: 

In the late 20th century, the emphasis shifted towards machine learning algorithms, which enabled computers to learn from data and improve over time.

Deep Learning Revolution: 

Recent advancements in deep learning, a subset of machine learning, have propelled AI to new heights, enabling breakthroughs in areas like image recognition, natural language processing, and robotics.

Emergence of Generative AI:

Definition: 

Generative AI, or Gen AI, is a type of artificial intelligence technology capable of producing diverse data types like text, images, videos, and audio by learning patterns from existing data and generating novel outputs.

Emergence of Generative AI and Workplace Examples:

Reasons for Emergence: Generative AI, which creates new content such as images, text, or audio, has emerged due to advancements in deep learning architectures like GANs (Generative Adversarial Networks).

Workplace Examples: 

Generative AI is increasingly used in creative fields like graphic design (e.g., generating artwork), content creation (e.g., generating articles or music), and product design (e.g., generating prototypes).

Key Example:ChatGPT: 

A prominent example of Generative AI, ChatGPT is a natural language processing tool widely utilized for generating human-like text responses from simple prompts, such as providing recipes or composing music.

Factors Driving Emergence:

Technology Accessibility: The widespread availability and use of technology have facilitated the development of Generative AI by aiding humans in daily tasks.

Data Availability: 

The abundance of data available to companies has enhanced the accuracy of Generative AI results by providing ample training data.

Analytical Tools: 

The proliferation of tools capable of analyzing vast datasets and identifying patterns has contributed to the emergence of Generative AI.

Generative AI's emergence marks a transformative shift in how we interact with technology, impacting various industries such as content creation, communication, and entertainment.

Difference between Artificial Intelligence and Automation:AI: 

Artificial Intelligence refers to the simulation of human intelligence processes by machines, including learning, reasoning, and self-correction.

Automation: 

Automation involves the use of technology to perform tasks with minimal human intervention, often following pre-programmed instructions. While AI can be a component of automation, not all automation involves AI.

Key Shifts in the Workplace Influenced by AI:

Automation of Routine Tasks: AI has led to the automation of repetitive and mundane tasks, freeing human workers to focus on more complex and creative endeavors.

Increased Efficiency: 

AI-powered tools and systems have improved efficiency and productivity in various industries, from manufacturing to healthcare.

Demand for New Skills: 

The integration of AI has led to a growing demand for skills related to data analysis, machine learning, and AI development.

Relationship between Artificial Intelligence and Humans:Augmentation: 

AI has the potential to augment human capabilities by automating repetitive tasks, providing insights from vast amounts of data, and enhancing decision-making processes.

Concerns: 

However, there are concerns about job displacement, ethical implications, and the need to ensure that AI systems are developed and deployed responsibly.

Personal Reflection on AI Usage:

As an AI, my experiences are focused on assisting users with tasks such as generating text, providing information, and facilitating communication. I continuously learn from interactions to improve my responses and capabilities.

Impacts of AI on Human Roles and Responsibilities:

Job Transformation: 

AI is likely to transform many industries and job roles, leading to the creation of new roles while reshaping existing ones.

Upskilling: 

Workers may need to acquire new skills to adapt to the changing demands of AI-enabled workplaces, such as data analysis, programming, and human-AI collaboration.

Future Skills for Working with AI:

Data Literacy: Understanding how to collect, analyze, and interpret data will be essential for working with AI.

AI Development: 

Proficiency in programming languages, machine learning algorithms, and AI development frameworks will be valuable.

Ethical Awareness: 

Knowledge of ethical considerations surrounding AI, such as bias mitigation and privacy protection, will be crucial.

Action Plan for Skill Development:

Self-Assessment: Evaluate current skills and identify areas for improvement, particularly those relevant to AI and data science.

Learning Resources: 

Utilize online courses, tutorials, and books to learn programming languages like Python, data analysis techniques, and AI concepts.

Hands-on Projects: 

Engage in practical projects to apply newly acquired skills, such as building AI models or analyzing real-world datasets.

Networking and Collaboration: 

Join AI communities, attend workshops, and collaborate with peers to gain insights, share knowledge, and stay updated on industry trends.

Continuous Learning: 

Embrace a mindset of continuous learning to keep pace with advancements in AI technology and adapt to evolving workplace requirements.

What's your 'digital footprint'?

Your 'digital footprint' refers to the information you disclose about yourself online. Many people use social media to exchange information and communicate with friends, family, and colleagues. It's worth looking at what others can find out about you. Why not start a new window and search Google for your own name? Are there any surprises? Do not share the results here! If you see anything unusual or unsettling, go back and review your social media accounts' privacy settings. Also, remember to use caution when adopting privacy settings on websites.

Accenture:

Accenture is a worldwide professional services company with market-leading capabilities in digital, cloud, and security, embracing change to create value and shared success for clients, employees, shareholders, partners, and communities.

Technologies behind AI:

Machine Learning: Utilizes algorithms to enable machines to learn from data and make predictions or decisions without being explicitly programmed.

Deep Learning: 

A subset of machine learning that involves neural networks with multiple layers, capable of learning complex patterns in large datasets.

Natural Language Processing (NLP): 

Enables machines to understand and generate human language, facilitating tasks like language translation, sentiment analysis, and text generation.

Computer Vision: 

Employs algorithms to enable machines to interpret and understand visual information from images or videos, enabling tasks like object recognition and image classification.

Benefits of AI:

Automation: 

AI enables automation of repetitive tasks, leading to increased efficiency and productivity.

Decision Making: 

AI algorithms can analyze vast amounts of data to provide insights and support decision-making processes.

Personalization: 

AI enables personalized recommendations and experiences tailored to individual preferences and behaviors.

Innovation: 

AI fosters innovation by enabling the development of new products, services, and solutions across various industries.

Considerations of AI:

Ethical Implications: AI raises ethical concerns related to bias, privacy, transparency, and accountability in decision-making processes.

Job Displacement: 

The automation enabled by AI may lead to job displacement in certain industries, requiring retraining and upskilling of the workforce.

Security Risks: 

AI systems are vulnerable to security threats such as hacking, manipulation of data, and adversarial attacks.

Social Impact: 

AI may exacerbate societal inequalities and contribute to social disruption if not implemented responsibly and equitably.

Importance of Responsible AI:

Ethical Frameworks: 

Responsible AI involves developing and adhering to ethical frameworks to ensure fairness, transparency, and accountability in AI systems.

Bias Mitigation: 

Addressing biases in AI algorithms and data to prevent discriminatory outcomes and promote inclusivity.

Regulation and Governance: 

Implementing regulations and governance mechanisms to oversee the development, deployment, and use of AI technologies.

Human-Centric Design: 

Prioritizing human values and well-being in the design and implementation of AI systems to enhance societal benefit and minimize harm.

Behind the scenes of AI: Sense


Facial Recognition:

Definition: 

Technology that can identify a person from an image or video using facial characteristics.

Application: 

Used for security purposes, authentication, surveillance, and personalized experiences in various industries.

Image Categorization:

Definition: 

The process of putting images into different categories for use within a training model.

Application: 

Enables image search, content organization, and object detection in fields like e-commerce and healthcare.

Sound Pattern Recognition:

Definition: 

The process of classifying sounds into different categories and recognizing patterns in the sounds.

Application: 

Used in speech recognition, acoustic event detection, and monitoring systems in industries such as automotive and healthcare.

Translating Speech to Text:

Definition: 

When technology turns spoken language/voice into a textual transcript.

Application: 

Facilitates voice-controlled interfaces, transcription services, and accessibility features for people with hearing impairments.

Computer Vision:

Definition: 

The ability to allow computers to see, recognize, and process images in the way that humans can through video or image analytics.

Application: 

Enables tasks like object detection, facial recognition, autonomous vehicles, and medical image analysis.

Audio Processing:

Definition: 

The analyzing of audio signals.

Application: 

Used for speech enhancement, noise reduction, audio recognition, and music analysis in various domains including telecommunications and entertainment.

Behind the scenes of AI: Comprehend

Natural Language Processing (NLP):

Definition: 

The ability of computer solutions to understand and interpret human language naturally.

Application: 

Used for sentiment analysis, language translation, text summarization, and virtual assistants like chatbots.

Knowledge Representation:

Definition:

Representing information about the world in a form that a computer system can use to solve a complex task.

Application: 

Facilitates reasoning, problem-solving, and decision-making in domains such as expert systems, semantic web, and artificial intelligence.

Speech Recognition:

Definition: 

The use of sound pattern recognition to understand if two voices are the same.

Application: 

Enables hands-free operation of devices, voice-controlled interfaces, dictation systems, and voice search.

Pattern Recognition:

Definition: 

The automated recognition of patterns or irregularities in data.

Application: 

Used in image analysis, medical diagnosis, fraud detection, and machine learning algorithms for identifying trends and anomalies.

Chatbots:

Definition: 

A computer program designed to simulate an intelligent conversation on a text or verbal basis.

Application: 

Used for customer service, information retrieval, task automation, and entertainment, providing personalized interactions and assistance.

Behind the scenes of AI: Act and learn

Deep Learning - A subset of Machine Learning where complex, multi-layered neural networks learn from large amounts of data. Neural networks are algorithms inspired by the structure and function of the brain which have led to revolutionary advances in Artificial Intelligence.

These five AI developments will shape 2021 and beyond

"Applied natural language processing

The trend—and why it matters. In 2020, natural language systems became significantly more advanced at processing aspects of human language like sentiment and intent, generating language that aligns with human speaking and writing patterns, and even visual understanding, meaning the capability to express understanding about an image through language. These natural language models are powering more accurate search results and more sophisticated chatbots and virtual assistants, leading to better user experiences and creating value for businesses.

Baidu’s innovations. Baidu released a new multiflow sequence framework for language generation called ERNIE-GEN. By training the model to predict semantically complete blocks of text, ERNIE-GEN performs at an elite level across a range of language generation tasks, including dialogue engagement, question generation, and abstractive summarization.

Baidu’s vision-language model ERNIE-ViL also achieved significant progress in visual understanding, ranking first on the VCR leaderboard, a dataset of 290,000 questions built by the University of Washington and the Allen Institute for AI, that aims to test visual understanding ability. ERNIE-ViL also achieved state-of-the-art performance on five vision-language downstream tasks. Visual understanding lays the foundation for computer systems to physically interact in everyday scenes, as it involves both understanding visual content and expressing it through language. It will be crucial for improving the quality of human-machine interaction.

"

Read the full article here: https://www.technologyreview.com/2021/01/14/1016122/these-five-ai-developments-will-shape-2021-and-beyond/

Artificial Intelligence vs Automation

"The terms artificial intelligence and automation are frequently used interchangeably. They both relate to software, physical robots, and other machines that allow us to be more efficient and effective. However the complexity levels of the two systems are very different.

Automation is making something run by itself with little or no interaction from humans by following patterns and rules to perform repetitive tasks.

Automation is everywhere and is used in everyday life without you even realizing it. When you receive a reminder email or text message for a specific appointment, this will most likely be an automated response, set up in a system beforehand. For example, when you book an appointment with the dentist and receive an email before your appointment, this is likely to be an automated response. This has made work simpler and easier for businesses, whilst offering its customers a more personalized experience. It has also allowed staff to have more time to dedicate to more interpersonal tasks, such as talking to customers and understanding their problems, as the forgetful and repetitive tasks (such as sending out a reminder email), are already taken care of!

Artificial intelligence is used to assist humans in non-repetitive tasks to find patterns, learn from experiences, and then using machine learning choose the correct responses. Unlike Automation, it does not follow orders or rules.

Artificial intelligence is used to provide insights. For example, imagine someone has a head injury and needs to determine the level of damage. An AI machine could help diagnose the degree of damage by being ‘trained’ on multiple X-rays of previous head injuries. It would then understand the severity of the current head injury and provide an informed result. This could help doctors provide their overall diagnosis for the patient a lot quicker, as they would already have gained the insight on the severity of the injury from the AI machine and would therefore have a better understanding of the patient’s condition before doing their checks."

(Source: Digital Skills: Artificial Intelligence  by Accenture at FutureLearn)

Benefits of Artificial Intelligence:

Automation: 

AI enables automation of repetitive tasks, improving efficiency and productivity.

Decision Support: 

AI algorithms analyze vast amounts of data to provide insights for better decision-making.

Personalization: 

AI enables personalized experiences and recommendations tailored to individual preferences.

Innovation: 

AI fosters innovation by enabling the development of new products, services, and solutions.

Considerations of Artificial Intelligence:

Ethical Implications: 

AI raises concerns about bias, privacy, transparency, and accountability in decision-making processes.

Job Displacement: 

Automation enabled by AI may lead to job displacement, requiring retraining and upskilling of the workforce.

Security Risks: 

AI systems are vulnerable to hacking, manipulation of data, and adversarial attacks.

Social Impact: 

AI may exacerbate societal inequalities and contribute to social disruption if not implemented responsibly and equitably.

Examples Requiring Extra Caution:

Autonomous Vehicles: 

AI-driven cars raise concerns about safety, liability, and ethical decisions in critical situations.

Healthcare Diagnosis: 

AI in healthcare requires careful validation to ensure accuracy, avoid misdiagnosis, and protect patient privacy.

Financial Markets: 

AI-driven trading algorithms can contribute to market volatility and pose systemic risks if not regulated properly.

Balancing Benefits and Concerns: 

While AI provides significant benefits, it is critical to address its concerns to reduce risks and ensure responsible adoption. By putting in place ethical frameworks, rules, and strong security measures, the benefits of AI can outweigh the drawbacks, resulting in revolutionary advances while reducing possible consequences.