#AIsystems

[waybackwanderer]

Ai Systems - Web Links Oct 1997 Archived Web Page 🧩

[cpapartners]

Small firms must balance budget and ambition on AI

Small firms can't drop billions on custom AI systems, but there are still ways to leverage the tech without breaking the bank.

[govindhtech]

Google Launches Gemini 2.0 AI Model In December 2024

Google Gemini 2.0 AI Model Overview

As artificial intelligence transforms technology, Google’s Gemini 2.0 AI model is expected to revolutionize AI and machine learning. It improves performance, accuracy, and versatility in AI applications across industries by addressing previous model constraints. Google’s latest AI model could improve machine-human interaction, understanding, and prediction in Natural Language Processing(NLP), computer vision, and data analysis. Google’s Gemini 2.0 AI model’s features, applications, and significance are examined in this article.

What Is the Google Gemini 2.0 AI Model?

Gemini 2.0 AI

Google created the initial Gemini model to compete with OpenAI’s GPT series and other powerful AI systems. The previous Gemini model achieved great progress, but Gemini 2.0 intends to improve Google AI technology by overcoming restrictions and increasing its applications. It is planned to push AI limits with increased training data, algorithms, and infrastructure.

What differentiates Gemini 2.0?

Multimodal learning, predictive accuracy, and energy efficiency are it’s main goals. This iteration optimizes for real-time applications and improves performance on huge datasets and complicated jobs. Google wants to create a powerful, resource-efficient AI model.

Key Features of Google Gemini 2.0

Multimodal abilities

Multimodal learning makes Gemini 2.0 intriguing. Gemini can process and analyze text, graphics, and audio, unlike prior models that concentrated on text. This makes it perfect for healthcare diagnostics and customer service automation that cross-reference data types.

Improved NLP

Refined NLP in Gemini 2.0 helps the model understand complicated linguistic patterns, idiomatic idioms, and subtle emotions. This helps the model have more contextually accurate discussions, interpret queries better, and respond coherently and empathetically. Virtual assistants, customer service bots, and content creation can benefit from this development.

Real-Time Data Processing

It handles high-speed data streams without sacrificing performance with real-time processing. It is useful for financial trading, autonomous driving, and logistics, which demand fast decisions. Google uses clever algorithms to keep the model accurate and responsive even while processing enormous amounts of data.

Sustainability and Energy Efficiency

Energy-hungry AI models are routinely criticized. Google’s Gemini 2.0 energy optimization advances address this. Gemini 2.0 uses sophisticated hardware and energy-efficient algorithms to accomplish complicated tasks with low power consumption, making it more sustainable for large-scale applications and contributing to Google’s green technology efforts.

Medical Care and Research

Its multimodal capabilities can transform medical diagnosis and Medical Care. Gemini 2.0 can help predict diagnoses and early health risk identification by processing medical imaging, lab results, and patient histories. This AI model can analyze complex datasets more efficiently, potentially speeding up drug discovery.

Virtual Assistants and Customer Support

It is perfect for customer care and virtual assistance because of its increased NLP. Better accuracy and human-like responses from this model reduce human involvement and improve user happiness. It helps e-commerce and banking companies identify client wants and give faster, more accurate answers.

Finance and Real-Time Analysis

The financial sector needs real-time data processing. It’s a real-time analytics process and interpret market data instantly, giving traders and analysts real-time insights. This functionality is necessary for high-frequency trading, fraud detection, and risk management.

Autonomous cars and robots

Gemini 2.0 will effect autonomous driving and robotics, where fast, reliable decision-making is essential. Its multimodal data processing lets autonomous systems understand visual, aural, and sensor input simultaneously, boosting safety and efficiency. Robotics applications can improve navigation, obstacle recognition, and dynamic environment interaction with it.

Digital Media and Content Creation

Gemini 2.0 is ideal for copywriting, video scripting, and social media management because to its enhanced language production and multimodal features. Digital marketers and content makers benefit from the model’s capacity to understand and create creative content that matches brand voice and audience expectations.

Gemini 2.0 Release Date

In December 2024, Google plans to release Gemini 2.0, the most recent iteration of their AI language model series. Building on Gemini 1.5, this release will try to give developers and end users better performance and capabilities. Broad access will probably be made possible by the deployment, enabling uptake and real-time feedback. Even while there have been rumors that Gemini model would fall short of Google’s internal performance targets, the upgrade is expected to have better multimodal capabilities, making it a formidable rival to OpenAI’s next model, Orio.

This Impacts AI’s Future

Google’s Gemini 2.0 is more than an upgrade it sets new AI versatility and efficiency norms. Gemini shows Google’s dedication to powerful, responsible AI by emphasizing multimodal learning, real-time processing, and sustainability. Its many applications promise to improve workflows, consumer experiences, and data-driven decision-making across industries.

Models like Gemini 2.0 enable smarter and more intuitive technological interactions, building a future where AI systems are part of how to work, play, and live.

Read more on Govindhtech.com

[sifytech]

Convergence Digital and Real - Is It Good

If we give algorithms total control over our decisions, they can influence what we eat and how we behave, our choices may be influenced by an entity we cannot control. Read More. https://www.sify.com/ai-analytics/convergence-digital-and-real-is-it-good/

[thxnews]

Facebook and Instagram Enhance User Control and Transparency in Content Ranking

  Enhancing User Control and Transparency

In a recent announcement, Facebook and Instagram unveiled significant updates to empower users with more control over their content experience and provide greater transparency into the algorithms shaping their feeds. The updates aim to make the platforms more user-friendly and address concerns regarding algorithmic influence. These changes come as billions of people rely on Facebook and Instagram to connect, share their lives, and discover captivating content.   Empowering Users with AI Systems Understanding the importance of personalization, both platforms utilize AI systems to curate content tailored to each user's preferences. By factoring in user choices and behavior, these systems attempt to deliver relevant and engaging content. In a prior discussion, Meta, the parent company of Facebook and Instagram, acknowledged the need for more transparency and user control, challenging the notion that algorithms render users powerless. Building on that commitment, Meta now takes strides toward openness and control.   Increased Transparency and Control Facebook and Instagram are committed to providing users with more transparency regarding AI systems that rank content across the platforms. By releasing 22 system cards, Meta grants insights into how these AI systems operate, the predictions they make to determine content relevance, and the available controls to customize the user experience. These system cards cover various sections such as Feed, Stories, Reels, and even unconnected content recommendations. Users can access the Transparency Center for a more detailed explanation of content recommendation AI. Moreover, Meta goes beyond system cards by sharing the types of signals and predictive models used to determine content relevance in the Facebook Feed. While the company aims to be transparent, it also recognizes the need to balance disclosure with safeguarding against misuse.   Personalizing the User Experience Recognizing that users have different preferences, Facebook and Instagram have centralized controls to customize content exposure. The Feed Preferences on Facebook and the Suggested Content Control Center on Instagram provide users with the ability to influence the content they see. Additionally, features like "Interested" and "Not Interested" on Instagram's Reels tab allow users to indicate their preferences and receive more of the content they enjoy. Facebook's "Show more, Show less" feature further empowers users to fine-tune their content consumption. For users desiring a more chronological feed experience, the Feeds tab on Facebook and the Following section on Instagram offer alternatives. Users can also create a Favorites list to ensure they never miss content from their favorite accounts.   Enabling Research and Innovation Meta believes in fostering openness and collaboration in the field of research and innovation, particularly regarding transformative AI technologies. Over the past decade, the company has released over 1,000 AI models, libraries, and data sets to support academic and public interest research. In the coming weeks, Meta will introduce the Content Library and API, offering comprehensive access to publicly-available content from Facebook and Instagram. Researchers from qualified institutions can apply for access, fostering scientific exploration while meeting new data-sharing and transparency obligations. By involving researchers early in the development process, Meta aims to receive valuable feedback, ensuring the tools align with their needs and aspirations. Facebook and Instagram's commitment to user control, transparency, and research collaboration signifies a forward-thinking approach, emphasizing the importance of customization and understanding in the ever-evolving landscape of social media platforms.   Sources: THX News & Meta. Read the full article

[nventrai]

AI’s Potential: Comparing Dynamic Retrieval and Model Customization in Language Models

Artificial Intelligence has come a long way in understanding and generating human language, thanks largely to advancements in large language models. Among the leading techniques that elevate these models’ capabilities are Retrieval-Augmented Generation and Fine-Tuning. Although both aim to improve AI responses, they do so through very different approaches, each with its own strengths, challenges, and ideal use cases.

The Basics: Tailoring Intelligence vs. Fetching Fresh Knowledge

At its core, Fine-Tuning is about customization. Starting with a broadly trained LLM, fine-tuning adjusts the model’s internal parameters using a specialized dataset. This helps the AI learn domain-specific terminology, nuances, and context, enabling it to understand and respond accurately within a particular field. For example, a fine-tuned model in healthcare would grasp medical abbreviations, treatment protocols, and patient communication subtleties far better than a general-purpose model.

In contrast, Retrieval-Augmented Generation enhances an AI’s answers by combining a pre-trained language model with a dynamic retrieval system. Instead of relying solely on what the model “knows” from training, RAG actively searches external knowledge bases or documents in real-time, pulling in up-to-date or proprietary information. This enables the AI to generate answers grounded in the latest data- even if that data wasn’t part of the original training corpus.

How Fine-Tuning Shapes AI Understanding

Fine-tuning involves carefully retraining the model on a curated dataset, often domain-specific. The process tweaks the model’s neural network weights to improve accuracy and reduce errors like hallucinations or irrelevant responses. Importantly, it uses a lower learning rate than the initial training to preserve the model’s general language capabilities while specializing it.

This method excels when the task demands deep familiarity with specialized language. For instance, healthcare fine-tuning enables the model to correctly interpret abbreviations like “MI” as “Myocardial Infarction” and provide contextually precise answers about diagnosis or treatment. However, fine-tuning can be resource-intensive and might not adapt quickly to new information after training.

Why RAG Brings Real-Time Intelligence

RAG models address a key limitation of static LLMs: outdated or missing knowledge. Since it retrieves relevant documents on demand, RAG allows AI to incorporate fresh, specific data into its responses. This is invaluable in fast-evolving domains or cases requiring access to confidential enterprise data not included during model training.

Imagine querying about the interactions of a novel drug in a healthcare assistant. A fine-tuned model may understand the medical terms well, but might lack details on the latest drug interactions. RAG can fetch current research, patient records, or updated guidelines instantly, enriching the answer with real-world, dynamic information.

The Power of Combining Both Approaches

The real magic happens when fine-tuning and RAG are combined. Fine-tuning equips the model with a strong grasp of domain language and concepts, while RAG supplements it with the freshest and most relevant data.

Returning to the healthcare example, the fine-tuned model decodes complex medical terminology and context, while the RAG system retrieves up-to-date clinical studies or patient data about the drug’s effects. Together, they produce responses that are both accurate in language and comprehensive in knowledge.

This hybrid strategy balances the strengths and weaknesses of each technique, offering an AI assistant capable of nuanced understanding and adaptive learning—perfect for industries with complex, evolving needs.

Practical Takeaways

Fine-Tuning is best when deep domain expertise and language understanding are critical, and training data is available.

RAG shines in scenarios needing up-to-the-minute information or when dealing with proprietary, external knowledge.

Combining them provides a robust solution that ensures both contextual precision and knowledge freshness.

Final Thoughts

Whether you prioritize specialization through fine-tuning or dynamic information retrieval with RAG, understanding their distinct roles helps you design more intelligent, responsive AI systems. And when combined, they open new horizons in creating AI that is both knowledgeable and adaptable—key for tackling complex real-world challenges.

[findurfuture]

Artificial Intelligence (AI) and Machine Learning (ML) have moved from being abstract ideas to real-world technologies that are reshaping how we live, work, and connect with the world around us. No longer confined to the realm of science fiction, AI and ML are now woven into the fabric of our daily lives. From revolutionizing healthcare to transforming how businesses operate, these technologies are driving changes we could hardly have imagined a few decades ago. But what exactly do AI and ML mean, and how are they shaping our future?

[placement-india]

𝐖𝐡𝐲 𝐀𝐈 𝐖𝐢𝐥𝐥 𝐍𝐞𝐯𝐞𝐫 𝐑𝐞𝐩𝐥𝐚𝐜𝐞 𝐘𝐨𝐮𝐫 𝐁𝐚𝐜𝐤 𝐎𝐟𝐟𝐢𝐜𝐞 𝐓𝐚𝐬𝐤𝐬?

The 𝐁𝐚𝐜𝐤 𝐎𝐟𝐟𝐢𝐜𝐞 is all about managing the internal operations and administrative tasks of the business. 👉It is quite important for its regular functioning.

The Jobs can include payroll processing, HR handling, and accounting. The digital tool gives adequate chances to automate the actions. However, technology is no doubt essential for the future of the business.

There is pressure to discover the right resources for in-house jobs. Get the full story: Click here👇 to read the full article. http://surl.li/tgrzq

[archoneddzs15]

Sega Saturn - Darius Gaiden

Title: Darius Gaiden / ダライアス外伝

Developer: Aisystem Tokyo

Publisher: Taito

Release date: 15 December 1995

Catalogue No.: T-1102G

Genre: Shooter

I was quite disappointed when I first received this game since I bought it when I lived in Ipoh (Perak) and paid RM 300 for it!! The first time I put it on I thought "Oh my God, I've just wasted 300 ringgit on a Super Famicom quality game" (the Western version was published by Acclaim). It wasn't until I sat down and played it, did I start to feel good about my purchase.

Yet again those mutant robotic fish are causing trouble and it's up to us to stop them. The game makes nice use of the Saturn's 2D powers which really shows in the poor PlayStation version converted by Interbec. There are some nice transparencies, warped backgrounds, and giant-sized enemies as well as that distinctive Zuntata (Taito's sound team) soundtrack.

Darius Gaiden isn't the best in terms of graphics however in the world of Darius it's not that bad at all. Most Darius games seem to look a bit basic. The game can easily hold its head up high with the best of them in terms of playability though such as Taito's own Metal Black or Technosoft's Thunder Force series.

[philosophiesde]

Zoomposium with Prof. Dr. Martin Bogdan: "When AI gets bored - Ways to (artificial) consciousness

Information about the person and research field

In this very exciting interview from our Zoomposium themed blog “Artificial intelligence and its consequences”, Axel and I talk this time with the German computer scientist Martin Bogdan, who conducts research on applied signal processing and data analysis in medicine and biology as well as embedded systems for bioanalog information processing at the Faculty of Mathematics and Computer Science in the Neuromorphic Information Processing Department at the University of Leipzig and works as Dean of Studies for Computer Science. It was precisely in this context that I became aware of him during my online research. I was looking for scientists, and in particular computer scientists, who work and research in the field of neuromorphic and bioanalog information processing. One reason for the research was that I have recently been dealing a lot with a possible paradigmshift from physics to biology in AI research. In this context, one could almost speak of a “biologization” in the development of new AIsystems. In this context of the possibilities of “communication” between biological-neuronal and artificial-neuronal networks, he has also worked a lot on new processor architectures. The old processor design for AI applications in the form of the functionalities of the hardware at circuit level (register transfer level synthesis) using “standard CMOS logic gates” is increasingly being replaced by artificial, neural networks “artificial neuronal network (ANN)” or “spiking neuronal networks (SNN)” in the course of “neuromorphic engineering” or “deeplearning (DL)” or perhaps replace them completely in the future. An attempt is made to translate the biological-neuronal networks of the brain into “spiking neuronal networks (SNN)” with the help of the Hodgkin-Huxley-Model. The action potentials of neurons and their connections are simulated as brain areas. Martin Bogdan also had another exciting but also provocative article on this topic, "Is Boredom an Indicator on the way to Singularity of Artificial Intelligence? Hypotheses as Thought-Provoking Impulse“ in 2023, in which he explores precisely this question of whether perhaps ”boredom“ in artificial intelligence could be a possible indicator for consciousness in the form of Kurzweil's ”Singularity". This is of course a “steep thesis”, which we had Mr. Bogdan explain to us in the not at all “boring” interview ;-). More at: https://youtu.be/izN9ac-9zw8

or: https://philosophies.de/index.php/2025/05/30/wenn-sich-ki-langweilt/

[govindhtech]

How IBM’s Smarter Balanced Is Governing Education AI

Smarter Balanced Assessment Consortium

The Smarter Balanced Assessment Consortium, a member-led public organization with headquarters in California, offers assessment tools to teachers in K–12 and higher education. Founded in 2010, the business creates creative, standards-aligned test assessment systems in collaboration with state education organizations. In order to assist educators in identifying learning opportunities and enhancing student learning, Smarter Balanced provides them with lessons, tools, and resources, such as formative, interim, and summative assessments.

In the constantly evolving field of education, Smarter Balanced is dedicated to progress and creativity. The objective is to investigate a systematic methodology for utilizing artificial intelligence (AI) in educational assessments in conjunction with IBM Consulting. The partnership is still in place as of early 2024.

Specifying the difficulty

Standardized exams and structured quizzes, which are common K–12 skill evaluations, are criticized for a number of equity-related reasons. AI has the revolutionary potential to improve assessment fairness across student populations, including marginalized groups, by providing individualized learning and assessment experiences when used responsibly. Therefore, defining what responsible AI adoption and governance in a school setting looks like is the main difficulty.

Educators, professionals in artificial intelligence, ethics and policy surrounding AI, and specialists in educational measurement made up the first multidisciplinary advisory group established by Smarter Balanced and IBM Consulting. The panel’s objective is to create guiding principles for integrating justice and accuracy into the application of AI to learning materials and educational measurement. Below is a summary of some of the advisory panel’s factors.

Considering human needs when designing

Organizations can create a human-centric strategy for implementing technology by utilizing design thinking frameworks. Design thinking is driven by three human-centered principles: a focus on user outcomes, restless reinvention, and team empowerment for diversity. Stakeholders’ strategic alignment and responsiveness to both functional and non-functional organizational governance requirements are enhanced by this approach. Developers and other stakeholders can generate creative solutions, prototype iteratively, and gain a thorough understanding of user demands by applying design thinking.

This methodology is critical for early risk identification and assessment during the development process, as well as for enabling the development of reliable and efficient AI models. Design thinking aids in the development of AI solutions that are mathematically sound, socially conscious, and human-centered by consistently interacting with various communities of domain experts and other stakeholders and taking their input into consideration.

Including Diversity

A varied group of subject-matter experts and thought leaders were assembled by the merged teams to form a think tank for the Smarter Balanced initiative. Experts in the domains of law and educational evaluation, as well as neurodivergent individuals, students, and those with accessibility issues, made up this group.

The think tank aims to iteratively, rather than one-time, integrate its members’ experiences, opinions, and areas of expertise into the governance framework. A fundamental tenet of IBM’s AI ethics is reflected in the strategy: artificial intelligence should supplement human intelligence, not replace it. Incorporating continuous feedback, assessment, and examination by a range of stakeholders can enhance the development of trust and facilitate fair results, ultimately resulting in an educational setting that is more comprehensive and productive.

In grade school settings, these approaches are essential for developing equitable and successful educational assessments. Building AI models that are reflective of all students requires the many perspectives, experiences, and cultural insights that diverse teams bring to the table. Because of its inclusivity, AI systems are less likely to unintentionally reinforce existing disparities or fail to take into account the particular demands of various demographic groups. This highlights another important AI ethical tenet at IBM: diversity in AI is important since it’s about math, not opinion.

Examining beliefs that are focused on the student

Determining the human values IBM want to see represented in AI models was one of the first projects that IBM Consulting and Smarter Balanced performed together. IBM arrived at a set of principles and criteria that correspond to IBM’s AI pillars, or essential characteristics for reliable AI, as this is not a novel ethical dilemma.

Explainability: The capacity to provide results and functions that don’t require technical explanation

Fairness: Handling individuals equally

Robustness: security, dependability, and ability to withstand hostile attacks

Openness: Sharing information about the use, functionality, and data of AI

Data privacy: revealing and defending users’ rights to their privacy and data

It is difficult to put these ideas into practice in any kind of organization. Even higher standards apply to an organization that evaluates pupils’ skill sets. Nonetheless, the work is valuable due to the potential advantages of AI. The second phase is now in progress and involves investigating and defining the values that will direct the use of AI to the assessment of young learners.

The teams are debating the following questions:

What morally-based guidelines are required to properly develop these skills?

Who should be in charge of operationalizing and governing them?

What guidance should practitioners providing these models with follow?

What are the essential needs, both functional and non-functional, and what is the required strength?

Investigating varying impact and levels of affect

IBM used the Layers of Effect design thinking framework for this activity. IBM Design for AI has donated numerous frameworks to the open source community Design Ethically. Stakeholders are asked to think about the primary, secondary, and tertiary implications of their experiences or goods using the Layers of Effect framework.

The intended and known impacts of the product in this case, an AI model are referred to as primary effects. One of the main functions of a social media platform, for instance, could be to link people with shared interests.

Although less deliberate, secondary impacts can swiftly gain importance among stakeholders. Using social media as an example, the platform’s value to advertisers may have a secondary effect.

Unintentional or unexpected consequences that show up gradually are known as tertiary effects. An example of this would be a social media platform’s propensity to provide more views to messages that are insulting or misleading.

The main (desired) consequence of the AI-enhanced test assessment system for this use case is a more effective, representative, and equitable tool that raises learning outcomes throughout the educational system.

Increasing efficiency and obtaining pertinent data to aid in more effective resource allocation where it is most required are possible secondary benefits.

Unintentional and possibly recognized tertiary effects exist. Stakeholders need to investigate what would constitute unintentional harm at this point.

The groups determined that there could be five types of serious harm:

Detrimental prejudice concerns that fail to take into account or assist pupils from marginalized groups who might require additional resources and viewpoints to meet their varied needs.

Problems with personally identifiable information (PII) and cybersecurity in educational systems where insufficient protocols are in place for their networks and devices.

Insufficient governance and regulations to guarantee AI models maintain their intended behaviors.

Inadequate communication regarding the planned usage of AI systems in schools with parents, students, instructors, and administrative staff. These messages ought to outline agency, like how to opt out, and safeguards against improper usage.

Restricted off-campus connectivity that could limit people’s access to technology and the use of AI that follows, especially in rural locations.

Disparate impact evaluations, which were first used in court cases, assist organizations in identifying possible biases. These evaluations look at how people from protected classes those who are vulnerable to discrimination on the basis of gender, ethnicity, religion, or other characteristics can be disproportionately impacted by policies and practices that appear to be neutral. These evaluations have shown to be useful in the formulation of employment, financing, and healthcare policies. IBM tried to take into account cohorts of students in their education use case who might, because of their circumstances, receive unequal results from tests.

The following were the categories found to be most vulnerable to possible harm:

People who experience mental health issues

People from a wider range of socioeconomic backgrounds, including those without a place to live

Individuals whose mother tongue is not English

Those with additional non-linguistic cultural factors

People with accessibility concerns or those who are neurodivergent

IBM group’s next series of exercises is to investigate ways to lessen these harms by utilizing additional design thinking frameworks, like ethical hacking. IBM will also go over the minimal specifications that companies looking to integrate AI into student assessments must meet.

Read more on govindhtech.com

[propicsmedia]

Why Are Famous Americans Trying to Save These Ostriches? Why Are Famous Americans Trying to Save These Ostriches? Watch the FULL EPISODE ON ProPIcs TV - Youtube BC Ostrich Cull Special Report - CAN AI SAVE MILLIONS OF ANIMALS AND BIRDS? About the Research and Development of leading-edge AI technology in the AG Tech industry, which can save billions in losses to farmers, ranchers and wildlife each year. How can these Ostriches play a vital role in the future of early detection and management of Avian Flu, Mad Cow and Bovine diseases among other animal outbreaks? Find out here.   #Saveourostriches #saveanimals #foodsupply #Avianflu #Bovineillness #Birdillness #Foodchain #disease #Health #Foodsafety #FoodInspection #ArtificialIntelligence #AGTech #AgriTech #Technology #Animalconservation #Animalwellness #Animalresearch #diseaseresearch #foodsafetyresearch #ai #AIsystems #TechnologySystems #futuretech #Ranchers #animalrescue #AnimalProtection #Birdcull #Birdculls #WorldHealthOrganization #DrOz #RobertFKennedy #RFK #TrumpAdministration #CanadianFoodInspectionAgency #breakingnews #FarmNews #RanchNews #vets #Agriculture #cows #Bulls #Herd #Horses #Pigs #news #WorldNews #technews #BritishColumbiaNews #newsupdate #WFP #WHO

[theaenetworks]

While workers are panicking that there will be an end to their jobs, for them artificial intelligence is up there for disruption not a sort of advancement in technology. Artificial intelligence having been around for years, has been relatively quiet, but has been the trend for conversation and the possible threat posed in all industries. Africa, moving slowly towards to the use of AI will be looking on the role it could to provide a near perfect development, not necessary to disrupt the workplace, while the fear has been is many European countries, it will be an assessment on how it could effect in many sectors across African countries.

It could be a case of technology getting solid and more sophisticated in providing clients with quality services to advance their products. The chatbot, which uses machine learning to respond to users, is aiding the process for workers by helping to write cover letters and resumes, to extend of generating ideas. This is as far as the technology can go and even further innovations will keep making it better, it is something everyone is looking to explore and how it tend to provide the requisite tool to advance products and services, and Africa cannot be afford to be left behind on this trend.

The point of disparity between AI and workers, is the grip that AI will chase them out of workplace and probably put them into extinction. Well, such suggestion might not be properly correct, as the emergence of the technology will actually positively impact workers’ daily lives and experience in the field of study and give room for them to improve their skill sets, also the effect it can have in the general work done in the company. It could be a way to expand and broaden ideas, ChatGPT strive on the ability to function like a personal assistant- generates text based on natural language processing to give an accessible and readable response.

It is about getting the work done and doing it very well, generative AI, is helping people to think outside the box, they marveled at efficiency which could help them with their findings and research. It is moving at snail pace in Africa, rarely getting into it, people are not fast aligning with the development, they are still reluctant on the change this technology can make while advancing the process, and it could be a point of change and leverage for students who struggle to find time to read, with the help of generative AI they will grow the passion to reinvent themselves and serve as a guide to make them better with what they do. Below is the difference between real image and AI-generated image

While many don’t feel convince to move towards generative AI. The ability to clone a voice of popular artistes to make songs have received backlash, and the industry is making all the efforts to regulate it, without interfering with their jobs. While, fear still grip employees about possible layoff if, artificial intelligence is fully embrace in the work place. While some are still scary and conceived it as a science fiction which is anchored on fictitious ideas. It is open to Africa top companies, to explore the gains it has to offer, from there it will descend to smaller business, and students began to embrace it, then it will be an advantageous tool.

https://anthonyemmanuel.com/the-role-of-artificial-intelligence-in-africa/

#AI #artificialintelligence #AISystem #technology #innovations #africa #tech