In 2020, 60% of Africa’s population was under 25 and its tech-savvy youth population is set to double by 2030, making up 42% of the world’s youth. This represents a significant opportunity to expand Africa’s tech talent pool, create broad-based jobs within the AI industry, and drive economic growth.

Africa’s AI ecosystem is home to more than 127 hubs with South Africa hosting the largest concentration (22%), followed by Nigeria (12%), Egypt (12%), and Kenya (10%) all of which play instrumental roles in overcoming barriers and accelerating economic and talent development.

The report highlights six components of a cluster driving AI innovations in Africa: grassroots AI communities, academia, human capital, policymakers, Big Tech, and investors. Among them, grassroots AI communities comprising data scientists and AI professionals have emerged as a strong nucleus for Africa’s AI landscape. Groups like Data Science Africa, Deep Learning Indaba, and Data Science Nigeria are shaping the future by building skills, showcasing African AI research globally, attracting investments, and creating jobs. The communities bring people and ideas together, connecting local talent with global opportunities, from international events to everyday WhatsApp chats, sparking growth and innovation across the continent.

Using qualitative and quantitative methods to uncover key insights, it is assessed that while the grassroots initiatives remain critical in bridging the continent’s AI skills gap, offering upskilling opportunities and job placements, limited resources constrain their potential. This highlights a collective drive by all components of innovation clusters is essential to advance a thriving ecosystem.

To achieve this, the report offers the following recommendations:

• Academia should expand AI programs, train more professors, and align university curricula with industry needs.
• Policymakers and African governments should develop comprehensive national AI strategies that balance innovation with ethical safeguards.
• The government should also prioritize infrastructure development such as reliable electricity, affordable internet, and better data access to support AI growth.
• Big Tech should foster fair partnerships that empower local ecosystems, prioritize knowledge transfer, and protect data sovereignty.
• Investors should diversify funding beyond health and agriculture to unlock AI’s potential in other critical sectors including education and financial inclusion.
• Donors should invest heavily in human capital development initiatives, particularly those focused on youth employment. They fund training programs, scholarships, and fellowships that aim to build a pipeline of skilled AI professionals.

Commenting on the report, Abbie Phatty-Jobe, Research and Engagement Manager at Caribou Digital, said AI harnessed collaboratively has the power to positively shape the African employment landscape and boost the economy.

“In collaboration with our research partners, we have explored emerging clusters within the distinct context of Africa to address critical challenges and accelerate development; their success depends on a collective strategic approach that tackles inclusivity and targeted investment in local talent and infrastructure,” Phatty-Jobe explained in a press statement. “By empowering grassroots communities, strengthening academia-industry ties, and fostering equitable partnerships, we can build an AI ecosystem that truly reflects Africa’s unique strengths and aspirations.”

Speaking about the key role of grassroots communities in driving innovations, Wadzi Comfort a researcher and digital economy expert, noted that the emergent AI innovation clusters across Africa showcase remarkable ingenuity and potential in the face of scarce resources.

“Tech-savvy, motivated young people; —our greatest asset emerging from Africa’s youth population boom; are spearheading local AI-powered solutions to address local challenges, demonstrating exceptional agency and creativity,” said Wadzi. “These innovations span a wide spectrum, including AI-powered diagnostic tools, Informal educational academies, Large Language Models (LLMs) in local languages, community-driven knowledge-sharing platforms, and collaborative tech convenings.”

Wadzi further says youth-driven initiatives and their innovators merit substantial support and resources to accompany their agency and foster their growth and impact.

Private investors, African governments, and donors not only provide crucial financial resources but also shape the direction of innovation by prioritizing specific areas of investment. Venture capital for DeepTech startups has soared from USD$86 million (approx. UGX315.33 billion) in 2015 to USD$1.2 billion in 2023, with over 300 investors—65% based in Africa—and 127 innovation hubs driving growth.

Key government initiatives, like Nigeria’s AI Research Scheme and South Africa’s AI Institute, alongside philanthropic support from the Mastercard Foundation and Bill & Melinda Gates Foundation, will keep creating an environment that addresses local challenges, drives innovation, and positions Africa at the forefront of AI technology.

The report employs the snowball research method to conduct in-depth interviews with 25 African AI experts, including policymakers, educators, and industry leaders, uncovering the state, challenges, and opportunities for AI innovation clusters. It also highlights insights from 18 young tech professionals involved in AI or tech fields from Zindi, Africa’s largest data science community, on their skills, job prospects, challenges, and AI’s impact on employment. Additionally, the report includes a comprehensive review of academic studies, policy documents, and reports on AI, innovation clusters, and youth employment across Africa.

Editor’s Note: To download the full report (HERE).

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Harnessing the power of AI in recruitment

In today’s digital recruitment landscape, many companies use advanced technology to streamline their hiring process. When you apply for a position and submit your CV with AI filtering, the information you provide is processed through a machine learning algorithm. Artificial Intelligence performs tasks like manually reviewing hundreds of resumes in mere seconds, sifting through candidates, and identifying potential matches based on data points. It saves valuable time and increases efficiency, allowing HR reps to focus on more critical aspects of recruiting.

Redefining talent acquisition

AI is reshaping how recruiters discover and engage with potential job candidates. Traditional recruitment involved a significant amount of time and effort scanning through countless resumes and profiles. AI-based sourcing tools, however, help recruiters tap into a broad talent pool by finding the right candidates within minutes. These tools build candidate profiles, score them based on their qualifications, and rank them according to relevancy. Additionally, AI enhances the candidate experience by enabling swift responses to queries and maintaining ongoing communication.

Reducing hiring bias

One critical challenge in recruitment is eliminating unconscious bias. AI helps address this issue by ensuring an objective evaluation of candidates. It eliminates the potential influence of factors like race, age, or gender that could sway a decision unfairly. By evaluating candidates solely on their qualifications and skills, AI leads to a more diverse workforce that accurately represents today’s multicultural society.

Improving retention rates

Artificial Intelligence isn’t only transforming how organizations recruit, but also how they retain talent. AI analytics help predict employee behaviors and identify factors that could trigger their departure. Consequently, employers can develop and implement retention strategies to ensure they maintain a satisfied and successful workforce.

Also read:

• • How AI contract review helps you identify key contract clauses
  • AI is unpredictable and the destruction is going to be big in fintech — Dave Birch
  • Transforming education with AI transcription

Impact on job seekers

AI isn’t just revolutionizing the employer’s role in recruitment. It assists job seekers with finding roles more suited to their skills and potential. Automated job match systems suggest roles based on the seeker’s experience, background, and preferences. Additionally, AI-driven interview platforms allow job seekers to virtually showcase their skills, enabling a flexible and time-efficient interview process.

Preparing for the AI era

With AI holding such immense potential, it’s crucial to understand how to navigate this new landscape. Employers should start with developing knowledge of AI tools and platforms that can streamline their recruitment process. Conducting studies to determine the impacts of AI on recruitment efficiency and bias reduction is beneficial.

Job seekers, on the other hand, should acknowledge that they’re not interacting merely with human recruiters. They must optimize their resumes for algorithms, focusing on keywords relevant to the job. Engaging actively with AI, such as chatbots, during the job application process can also be advantageous.

The future of AI in recruitment

As we move further into the digital age, the utilization of AI in recruitment will only increase. The technology is set to continually evolve and make the hiring process more efficient and fair. Things like pre-screening video interviews using set questions and automated responses will be more common. AI chatbots may even replace initial human interaction in vetting job applicants.

In an economy where talent is a fundamental asset, AI is a game-changer. Both employers and job seekers benefit from its objective, rapid, and effective processes. However, constant vigilance is crucial in managing the potentially intrusive effects of AI, ensuring its ethical and responsible use in recruitment.

In conclusion, technology is rapidly redefining the field of recruitment, with AI at the forefront. Leveraging these powerful tools can spur companies’ progression, aid in acquiring the right talent, and ultimately, foster growth. To job seekers, AI offers an innovative, efficient navigation in their quest for the perfect job fit. As the role of AI in recruitment continues to grow, it’s clear we’re only at the starting point of what promises to be an exciting journey.

The post The Role of AI in Transforming Recruitment: What Employers and Job Seekers Need to Know appeared first on PC Tech Magazine.

TensorFlow has a large library as well as a glass firewall and is one of the easiest ways of solving real-world problems. IT specialists can apply it in a broad range of spheres including image recognition, natural language processing, robotics, and workflow automation solutions, making it a natural fit for diverse industries seeking advanced automation and AI integration.

9 Fundamental Concepts

The following are the nine major concepts of TensorFlow:

1. Tensors: Tensors are the core part of TensorFlow. They are multi-dimensional arrays.  Tensors let you store and modify data. You may think tensors are a generalization of numbers, vectors, and matrices used in computations.
2. Graphs: TensorFlow makes use of computational graphs. These help represent and execute operations. This structure ensures efficient execution and also helps in easy debugging.
3. Sessions: Sessions in TensorFlow were initially used to execute computation graphs, providing a structured way to run operations. While eager execution replaced sessions in later versions, understanding sessions can help beginners grasp the underlying mechanics of TensorFlow, especially in NLP development workflows. This foundational knowledge is crucial for optimizing tasks like data preprocessing, model training, and deploying NLP solutions efficiently.
4. Eager Execution: Eager execution is a mode where TensorFlow operations are run immediately. This makes debugging easier. It simplifies coding for beginners. Thus, it provides instant feedback as it eliminates the need to build and run graphs explicitly.
5. Placeholders and Variables: Weights and bias are stored in variables during training, and data, which was previously provided as input to the models, is fed through placeholders. These concepts help any TensorFlow expert adequately control data flow.
6. Layers: Layers are the most fundamental element in TensorFlow neural network structures. They all work in layers; that is, CNN does convolution while RNN does recurrence in an attempt to help the model learn.
7. Optimizers: Different optimizers change the weight of a model in a way that lowers the error. Different optimization algorithms such as SGD, Adam, and RMSProp available in TensorFlow help identify the best set of parameters during the training process.
8. Loss Functions: The loss functions are a way to define the difference or discrepancy between the actual and predicted outputs. TensorFlow also comprises standard loss functions such as the mean squared error and cross-entropy, which are essential for teaching the model.
9. Model Training and Evaluation: Training involves feeding data into the model, measuring the loss, and adjusting the weights to improve performance. The evaluation uses unseen data to test the model’s robustness and ensure it generalizes well. TensorFlow simplifies these steps with its user-friendly API and built-in functions, accelerating product modernization services by enabling the efficient development of cutting-edge AI models. This streamlined approach ensures businesses can seamlessly integrate modern solutions into their workflows.

3 Uses of TensorFlow

Any leading TensorFlow development company will be an expert in different applications of TensorFlow. Three of them are as follows:

1. Image recognition and processing

Some typical applications of TensorFlow include Object recognition, image categorization, and segmentation. For example:

• Recognizing objects in security video surveillance.
• Classifying medical images, like detecting tumors in X-rays or MRIs.
• Increasing scene image resolution using super-resolution models.

2. Natural language processing

TensorFlow should be used to build natural language processing that aids in understanding and processing human language. Use cases include:

• Sentiment analysis for customer feedback.
• Other industrial applications include translation from English to Spanish.
• They build chatbots or virtual assistants to make the virtual agents capable of comprehending user queries.

3. Predictive analytics

TensorFlow can model complex patterns in large datasets, making it suitable for prediction tasks like:

• Analyzing, for example, likely future sales based on prior sales data or probable future trends in the stock market.
• Using anomaly detection to predict equipment failure in manufacturing.
• Applying a user behavior analysis to make recommendations for products on e-commerce websites.

In conclusion, tensorFlow provides a robust framework for new AI developers to dive into machine learning and deep learning. By mastering its core concepts—tensors, graphs, Keras, and more—you can create innovative models and solve complex problems. TensorFlow can transform your AI ideas into impactful real-world solutions with practice and persistence.

See also: Best use cases for TensorFlow in actual-world applications

The post Exploring the Fundamentals of TensorFlow for New AI Developers appeared first on PC Tech Magazine.

The contemporary methods of detection, such as frame inspection or pixel analysis, are deemed unsuitable. The problem is that the AI used for deepfake detection needs to improve along with the AI used for deepfake development. Keeping up with the aforementioned differences between authentic and fake content requires constant innovation and new techniques to avoid falling farther behind the pace of these developments.

How does deepfake detection technology work

There are several ways to detect AI-generated deepfake videos. These can be detected by observing the visuals of the content. Some unnatural movements in the video can help identify the spoof. Besides, the edges of the face in a deepfake video are blurry and unclear. It can also be identified by closely looking into the muscle movements while smiling and blinking. Also, speech patterns can contribute to the detection of deepfake videos. A real human has some natural variations that are difficult to imitate by the AI-generated video.

Moreover, there are techniques like machine learning and deep learning that can lend a hand to the detection of a deepfake. In machine learning, the modal is trained by using fake and real videos so that the system can learn the difference between both. Later, during the process, the system indicates even the trivial spoofs of AI-generated content. Furthermore, deep learning can also be used for the detection. It works by using a large database and samples. It is trained to analyze videos and images based on existing datasets. This system can detect alterations that are unable to be identified by the naked eye.

Applications of deepfake detection software

Deepfakes are being produced and used for a variety of reasons in many different domains. Deepfake spotting can be applied to a variety of fields, including forensics, fraud detection, security, and disinformation identification.

• Deepfake prevention is crucial in mitigating crimes. A criminal can create a deepfake video to harass or bully someone by using their personal information or creating fake videos. By investigating culprits, this fraud can be averted timely.
• AI deepfakes can be easily used to spread misinformation. For instance, a deepfake of a politician can be used to manipulate the audience. It can create panic and mistrust among the people. By addressing this issue, organizations can prevent the spread of misinformation.
• Online deepfake detection can help prevent many frauds and scams. Criminals are using fake videos to harass women and girls, impersonating them, and committing several other crimes that need to be addressed as soon as possible.
• Many legal cases require evidence in the form of audio, video, and image. The opponents can use deepfake images, audio, and videos to win their cases. The detection tools can help verify whether the content is real or fake.

Also read:

• • How financial institutions can safeguard against deepfakes
  • How do AI face swap and image-to-video tools compare for video creation
  • The role of 3D liveness detection in financial security

Detection tools empowered by AI

Fighting deepfake assaults requires the use of detecting technologies powered by artificial intelligence. The most recent machine learning algorithms can utilize these techniques to scan digital content for precise modification indicators that are invisible to the human eye. Additionally, by examining the patterns and differences in the videos and photographs, certain artificial algorithms may effectively identify deepfake content. The precision and dependability of the detecting techniques will rise as artificial intelligence technology continues to advance. Additionally, these enhancements will be essential in contrast to deepfake attacks. These technologies are also essential for law enforcement, online media outlets, and anybody else who needs to authenticate and verify digital content.

Advancements in deepfake detection technology over time

AI prevention solutions must be continuously updated to reflect the most recent methods of deepfake production. Therefore, despite its seeming great promise, several obstacles still stand in the way of its widespread adoption.

Therefore, collaborative AI models with quickly developing deepfake technology are the way of the future for deepfake detection. For example, these stand-alone systems would use speech recognition, image recognition, and behavior analysis to develop a comprehensive approach to detection; the challenge has been developing such a system.

Addressing issues like deep fake variety across media kinds and achieving widespread success with cross-platform integration without cultural and language sensitivity are difficulties that need collaboration between platforms, governments, and AI professionals.

Evolution in technology, including advancements like liveness detection, can bring creativity, but it should not lead to the invasion of privacy. Advancements and developments are crucial for growth, ensuring they do not result in the misuse of anyone’s data. It is also important to establish ethical guidelines and regulations alongside this evolution to prevent negative outcomes.

The post A Comprehensive Review of How Deepfake Detection Technology Works appeared first on PC Tech Magazine.

In this stage of the hype cycle, an investment bubble can form as businesses are promised that the new technology will transform every aspect of their operations for the better. And what results is a number of impressive success stories and scores of dismal failures.

While generative AI does appear to present many exciting opportunities, it’s easy to become fatigued by the relentless hype; especially if you’re one of the many businesses that has deployed AI with high hopes but haven’t achieved the results you were after. But the problem with any kind of hype is that when everyone is talking about something, we tend to want to try it out so that we can be part of the conversation too. This isn’t necessarily a bad thing as long as the investments you make align with your business strategy and you have a clear roadmap that outlines how you’re going to use AI to reach broader business goals.

A balanced approach

In 2024, it’s more important than ever to cut through the noise and adopt AI in a way that aligns with real business needs.

AI should not be adopted just because it’s trendy.

It should be deployed because it’s the right solution to address a specific problem. As such, it may be a better idea for you to channel your focus and budget to restructuring your existing databases or improving current processes because this offers better returns than an AI implementation.

In addition to this, when developing your AI strategy, it’s vital to remember that AI is not the only way to solve a problem. Your AI deployment should complement and not replace humans.

During the industrial revolution, we saw new machines and new ways of organising work transform entire industries, making them more productive and efficient. But the machines didn’t take over entirely because the human touch is critical. With this in mind, modern businesses need to find a way to strike a happy balance between leveraging AI for efficiency, while also maintaining human oversight so that they can guarantee that the technology delivers real, sustainable value.

This, in large part, comes down to having the necessary expertise to get the most out of AI; especially when dealing with legacy systems. If, for example, you get swept along by the AI hype but your company’s data landscape isn’t in great shape, you’re going to hit some roadblocks. Put simply, if you lack clean, usable data, AI won’t deliver the expected benefits. Before embarking on any AI implementation, be mindful of the fact that data quality has a massive impact on model performance. So, if you have data issues, these should be addressed upfront.

Right now, the challenge many businesses face is when to pull the trigger. While early adoption comes with higher upfront costs and greater risks, those that get involved early are the first to overcome the initial hurdles and really start experiencing the benefits of this new technology.

Conversely, the businesses that hang back a little can draw on the experience of the early adopters; understanding how the innovation can be used to good effect and, just as importantly, where it adds little or no value.

Whatever you decide, it all comes down to implementing AI thoughtfully and with a clear plan in place so that you can realise the potential of this technology when the time is right.

And always remember that AI is an enabler, not a solution in itself.

Editor’s Note: This article was written by Saša Slankamenac, Architect in the office of the CTO and AI lead at Dariel Software

The post OP-ED: Avoid the Hype to Unlock Real Value From AI appeared first on PC Tech Magazine.

Major TensorFlow benefits;

In the table given below, we shall focus on some of the advantages of TensorFlow:

In the chart given below, you will gain a better understanding of the GitHub Star count of some of the best open-source deep learning frameworks. Just look at TensorFlow:

Source

Real-world examples of TensorFlow in action

TensorFlow is an open-source machine library that is best suited to handle real-world business applications. The real-world examples of TensorFlow in action are listed below:

• Detecting Text and Categorizing

The discussion of some of the most popular use cases associated with deep learning cannot be complete without mentioning its text-based applications. Sentiment analysis, detecting fraud, and identifying potential threats are standard text-based applications related to deep learning. The presence of TensorFlow in sentiment analysis is very useful in social media marketing and customer relationship management. Likewise, fraud detection is also capable of supporting operations carried out in financial and insurance domains.

Deep learning powers sentiment analysis, fraud detection, and threat identification, driving innovation across industries. To implement these solutions effectively, hire Flask developers for creating robust web platforms tailored to your needs.

The text-related application of TensorFlow also prioritizes the detection of language. For example, an individual will be able to locate support for multiple languages via Google Translate. The use cases of TensorFlow also feature summarization of text. Internet giant Google has discovered that a deep learning method known by the name sequence-to-sequence or in short S2S learning helps carry out text summarization. Creating news headlines is one of the applications of the S2S deep learning technique associated with TensorFlow. Besides, SmartReply, which can create email responses instantly is yet another best example of TensorFlow use cases related to text-related applications.

• Time Series Algorithms

The use of TensorFlow also happens with time series algorithms, as it helps analyze time series data. In certain circumstances, you may consider availing the services of a TensorFlow-certified developer to retrieve relevant statistics from the time series data. For example, TensorFlow is an ideal choice to forecast the stock market.

Today, some of the major platforms such as Netflix, Facebook, Amazon, and Google take advantage of deep learning to provide suggestions to users. An exact picture of the preferences and expectations of a customer can be provided by this module. For example, a TensorFlow deep learning framework permits recommendation engines to suggest certain TV shows or movies based on an individual’s habits. They also support use cases in various other sectors such as finance, governance, security, IoT, and accounting.

• Image Recognition

The use of image recognition contained in deep learning has a significant role in boosting the popularity of machine learning and enhanced learning. Some of the noteworthy users of image recognition applications consist of smartphone manufacturing firms, media, and telecom. Apart from that, image recognition can support usage that features photo clustering, image search, face recognition, and motion detection as well. The use cases of TensorFlow are best suited for applications such as automobile and healthcare industries. The presence of image recognition features in TensorFlow is very useful in getting familiar with the context along with image content. It is an ideal option for creating object recognition algorithms too.

See also: The ethics of using facial recognition technology

• Detecting Videos

Video detection is yet another notable and handy feature included in TensorFlow. The deep learning algorithms also assist in motion detection along with real-time thread detection in gaming and security. For the past several years, researchers have been actively involved in massive-scale datasets for video categorization. Youtube is one of the finest examples of datasets. These datasets are very useful in speeding up the process of research related to modeling of noisy-data, and transfer learning.

• Voice Recognition Applications

In addition to the above-mentioned, voice recognition algorithms are one of the best use cases of deep learning. TensorFlow can support the use of deep learning for various voice search applications. It is currently quite popular amongst manufacturers of smartphones and telecom giants. Some companies may even rely on a reliable TensorFlow developer to deal with sentiment analysis in CRS-based applications. Apart from that, they are best suited for the automobile and aviation industry, as they help in tracking errors at an early stage including unusual noise of engines.

Leverage TensorFlow for voice recognition and sentiment analysis with expert Python developers. Hire Python developers to power your next innovative application.

• Recommender Systems

Recommender Systems can provide recommendations in a personalized format to individuals based on their expectations. TensorFlow provides numerous libraries along with modules that would assist you in designing recommender systems. Some of them include TensorFlow Recommenders for building and gauging models, and TensorFlow Serving for employing models. In addition, TensorFlow Ranking to learn how to rank items is also available. Content recommendation, product recommendation, and ad recommendation are some of the most popular recommender systems use cases of TensorFlow.

The use cases of TensorFlow highlight the fact that it is one of the best tools for software developers who are focusing on deep learning algorithms. Being a highly advanced subset of AI, deep learning is very helpful in developing the upcoming generation of smart applications as well as systems. The use of TensorFlow within deep learning would ease the ML development/deployment process while offering maximum scalability for development along with visualization.

The post Best Use Cases for TensorFlow in Actual-World Applications appeared first on PC Tech Magazine.

According to a source cited by Bloomberg, this new version to be released in 2026 is aimed at catching up with OpenAI’s chatbot, ChatGPT and will be similar to OpenAI’s Advanced Voice Mode but with all the same access to personal information and apps that Siri currently has.

The enhanced version, tentatively called “LLM Siri,” will fully replace the Siri interface that users currently rely on and is expected to be better at handling complex tasks, interacting with third-party apps through App Intents, and performing functions like summarizing and writing text using Apple Intelligence.

At the moment, Apple is relying on third parties to power the iPhone’s advanced AI features. While ChatGPT will become available inside Apple Intelligence in December, Apple has reportedly discussed similar deals with other AI providers, like Google and Anthropic.

The tech giant also debuted its much-ballyhooed Apple Intelligence platform last month, though it still lacks many of the features offered by other tech companies.

The post Apple is Building a More Conversational Version of Siri appeared first on PC Tech Magazine.

Speaking at the first data analytics, artificial intelligence, and data governance symposium in Kampala, Tayebwa said he would ensure Uganda catches up with technological development. “Many of our youths in the private sector have run ahead and are already benefitting from AI even in the absence of enabling laws,” he stated. Adding “As a government, we need to work faster to own and manage local data and empower Ugandans to use it to make data-driven decisions”.

He underscored the need to establish a law to regulate the use of artificial intelligence in the country, calling for the strengthening of existing data protection laws at both the continental and national levels for the benefit of users.

In his key note address at the Symposium, Dr. Fred Muhumuza, a senior economist and development expert, said government and Ugandans need to have access to reliable big data to make informed decisions. He highlighted the importance and urgency of data integration and interrogation, especially in the policy environment to enable Social-Economic Transformation in Uganda.

The symposium which brought together over 150 CEOs and top executives from diverse sectors including media, banking, fintech, government, agencies and private sector, was themed; “The role of Data Analytics and Artificial Intelligence in social-economic transformation”.

The symposium convener and CEO of Task Managers Ltd Arthur Arinaitwe said the event had been in the offing since 2018, to bring together industry experts to discuss data usage and management, while enabling businesses to fasten and make more accurate decisions to offer better solutions sustainably to their customers.

The post Deputy Speaker of Parliament, Rt. Hon. Thomas Tayebwa Calls For Regulations on Data Use and AI appeared first on PC Tech Magazine.

First announced as a prototype called SearchGPT in July this year, the company wrote in a blog post “Getting answers on the web can take a lot of effort, often requiring multiple attempts to get relevant results.” Adding “We believe that by enhancing the conversational capabilities of our models with real-time information from the web, finding what you’re looking for can be faster and easier.”

ChatGPT users can now search the web via the platform and get timely answers with links to relevant web sources attached, eliminating the need to use search engines like Google or Microsoft Bing.

“This blends the benefits of a natural language interface with the value of up-to-date sports scores, news, stock quotes, and more,” said OpenAI. “The chatbot will choose to search the web based on what you ask, or you can manually choose to search by clicking the web search icon.”

Powered by a fine-tuned version of OpenAI’s GPT-4o model, Search will help bridge the gap between multiple searches and digging through links to find quality sources and the right information. All a user needs to do is ask a question in a more natural, conversational way, and the feature will respond with a better answer based on the information from the web. In instances where users have follow-up questions, the functionality will consider the full context of the chat and then provide a better result.

According to the blog post, the company has partnered with news and data providers to add up-to-date information and new visual designs for categories including weather, stocks, sports, news, and maps.

OpenAI further added that they have collaborated extensively with the news industry and carefully listened to feedback from their global publisher partners including Associated Press, Axel Springer, Conde Nast, Dotdash Meredith, Financial Times, GEDI, Hearst, Le Monde, News Corp, Prisa (El País), Reuters, The Atlantic, Time, and Vox Media.

Search is available across all ChatGPT platforms including iOS, Android, and desktop apps for macOS and Windows. However, the functionality is currently only accessible to ChatGPT Plus and Team users, plus SearchGPT waitlist users. The company says both Enterprise and Edu users will get access in the next few weeks while free users will gain access in the coming months.

The post OpenAI Launches its Search Engine “ChatGPT Search” appeared first on PC Tech Magazine.

AI-powered contract review offers a benefit in terms of saving time compared to methods that rely on manual review processes that are time-consuming and require meticulous scrutiny of every clause by human reviewers. With AI for content review, this task is streamlined significantly and completed faster than humans can. Moreover, using AI minimizes the chances of mistakes and oversights, ensuring that important details are noticed.

One advantage of utilizing AI is its cost-efficiency factor. This allows businesses to optimize resource allocation by automating tasks and effectively minimizing manual labor costs. It also enables skilled professionals to concentrate on intricate contract details that require their expertise the most.

How AI Identifies Key Clauses

AI systems use machine learning to effectively identify and extract clauses from text documents, employing natural language processing and advanced analytics techniques. By analyzing terms associated with obligations, liabilities, confidentiality, and other factors, these systems enable businesses to promptly evaluate contracts to ensure they are in line with objectives and guidelines.

Furthermore,​ artificial intelligence provides customization features by adjusting to the requirements of industries,​ whether it be real estate​, healthcare​, or technology agreements​. AI can be programmed to focus on clauses that are distinct to each sector​. This adaptability guarantees that companies receive suggestions that improve contract management.

Enhancing compliance and risk management

Ensuring organizations prioritize compliance with regulatory standards is crucial in today’s business landscape. AI for contract review aids in this effort by verifying that contracts align with required regulations. By detecting clauses related to compliance, AI assists in reducing the risks linked to legal violations.

Moreover, AI aids in risk management by identifying clauses that benefit a company. When faced with terms or unfavorable circumstances, AI points out these areas to help companies prepare in advance. This forward-thinking strategy can help prevent conflicts and financial risks.

Boosting efficiency in contract negotiations

During negotiations, time plays a role in the decision-making process. The use of AI helps speed up negotiations by offering a view of contract terms from the start. AI quickly highlights clauses, giving negotiators the necessary insights to make well-informed choices.

Efficiency also applies to swiftly making changes to contracts using AI technology when needed adjustments occur to verify that they match the agreements made in place. This adaptability in managing modifications is highly beneficial in changing business settings where flexibility is crucial.

Fostering collaboration and transparency

Utilizing AI encourages teamwork through a platform designed for contract management where team members can collaboratively access and review contracts in time to foster transparency during the entire process. This feature helps ensure that everyone involved stays informed to minimize confusion and improve communication.

Furthermore, reports created by AI provide information that helps teams make decisions effectively. By presenting data concisely, AI enables teams to have conversations that ultimately result in improved results.

In summary, the advent of AI contract review services is revolutionizing businesses’ approach to contract handling. These services combine efficiency with accuracy and flexibility, seamlessly and swiftly identifying clauses to empower organizations in managing contracts. In a fast-paced environment, prioritizing time and precision and utilizing artificial intelligence in contract review is not just advantageous but necessary for maintaining competitiveness. The forthcoming era of contract administration hinges upon the seamless assimilation of AI technology, facilitating intelligent decision-making processes and enhancing operational efficiency.

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