AI Systems Archives - Liwaiwai

Why Are Humans Afraid Of AI?

Humans may be afraid of AI for several reasons. Here are some of the top anxieties people worry about. Loss of Control: AI systems can become more intelligent and capable than humans, potentially leading to a loss of control over these systems, which can be concerning. Job Displacement: As AI continues to improve and automate tasks, there is a fear that many jobs will become obsolete, leading to unemployment and social unrest. Lack of Understanding: AI can be complex and difficult to understand for many people, leading to fear of the unknown. Ethical Concerns: There are concerns about how AI…

The Geography Of Artificial Intelligence

There are some geographical advantages for nations when it comes to AI. Here are some of the leading levers geography is playing a role in. Access to Talent: Nations with top-tier universities and research institutions have an edge in attracting and developing AI talent. These institutions can foster a strong AI ecosystem, providing a solid foundation for AI research and development. Technological Infrastructure: Countries with well-developed technology infrastructure, including high-speed internet, data centers, and cloud computing services, can better support AI research, development, and deployment. Data Availability: Nations with large populations and widespread use of digital technologies generate massive amounts…

Nature Already Inspired A.I. Than Most Realise

Nature offers many valuable insights and inspirations for understanding AI and developing new algorithms and techniques. Here are some of the key lessons learned from nature are the following. Adaptability: Living organisms are highly adaptable to changing environments, a quality that can be emulated in AI systems to improve their robustness and flexibility. Decentralization: Natural systems, such as ant colonies or bird flocks, often exhibit collective intelligence and robustness through decentralized decision-making. This can inspire the development of decentralized AI systems and swarm intelligence algorithms. Efficiency: Nature is often efficient in the use of resources and energy. Studying natural processes…

A S.W.O.T. Analysis Of Current A.I. Systems

AI, as a technology, has unique strengths, weaknesses, opportunities, and threats compared to preceding technologies. Here is a breakdown of how AI’s SWOT analysis differs. Strengths: – Learning capability: AI systems, especially machine learning models, can learn and improve from data over time, which sets them apart from traditional rule-based systems. – Versatility: AI can be applied to a wide range of domains, such as natural language processing, computer vision, robotics, and healthcare, making it a more versatile technology compared to many preceding ones. – Scalability: AI systems can process large amounts of data and scale to handle complex tasks,…

Artificial Intelligence

5 Ways To Avoid Artificial Intelligence Bias With ‘Responsible AI’

AI is an immense opportunity for humankind and many organizations. Having a clear AI governance framework based on clear principles will ensure that AI is used responsibly and optimally for an organization. Over the last few years, responsible AI has gone from a niche concept to a constant headline. Responsible, trustworthy AI is the subject of several documentaries, books, and conferences. The more we make responsible AI an expectation and a known commodity, the more likely we are to make it our reality. This enables us to flourish with more accessible AI. This is our shared goal through the Responsible AI Badge certification programme for…

Why Trust Matters In AI

We can all agree that AI has the potential to help businesses, organizations, and society solve real problems. But there are still many concerns about the consequences of using AI improperly. Things like ethics, privacy, bias, and security are top of mind. In order for AI projects to be fully embraced, companies must address these concerns, because people need to trust their AI in order for projects to be successful. In this installment of our blog post series, we will explore the question of why trust is an essential component to any conversation around AI. Performance, Operations, and Ethics Trust in…

How To Create Trust In Artificial Intelligence Using Open Source

Opening up the “black box” helps remove uncertainties about AI outcomes, providing insight into the modeling process and identifying biases and errors. Artificial intelligence (AI) is being used more frequently in our daily lives, with systems such as Siri and Alexa becoming commonplace in many households. Many households themselves are “smart,” powered by devices that can control your lights, heating and air, and even the music playing. And those music players are powered by AI that recommends songs and artists you may like. However, these systems are often referred to as “black box” systems because we do not know how…

Responsible Machine Learning Can Still Protect Intellectual Property – Here’s How

New documentation standards in machine learning can enable responsible technology. These risk management strategies highlight how organizations can be compliant while protecting their valuable intellectual property. The growing use of machine learning (ML), within a global drive toward digital acceleration, raises the question, “Who’s minding the store?” We’ve seen many examples of both the power and pitfalls of ML models. While organizations are increasingly aware of the downsides (our research shows many are still behind the curve), they find themselves trying to manage these risks without a set of agreed upon tools or standards. Meanwhile, they’re keenly aware of the competitive…

Artificial Intelligence: Multimillennial Data Transmitted To Machines With Brains

Artificial intelligence feeds on data, and data is piling up from increasingly cheap sensors and surging Internet use: videos, images, text; time series data, machine data; structured, unstructured and semi-structured data. And while AI is currently confined to narrow problems in discreet domains, the ambition of machine-learning researchers globally is to write algorithms that can cross domains, transferring learning from one kind of data to another. Show a computer vision system millions of X-rays of confirmed lung cancer patients and the system will become an expert at diagnosing lung cancer from X-rays. Around the world, hordes of unskilled workers are annotating…

Adversarial Machine Learning And Its Role In Fooling AI

Can you fool Artificial Intelligence? No? Think again. Three years ago, Apple launched IphoneX with cutting-edge facial recognition technology. This advanced AI technique (Face ID) replaced the old fingerprint recognition technology (Touch ID). The latest technology claimed to be more secure and robust. However, shortly after the launch of Face ID, researchers from Vietnam breached it by designing a 3D face mask. Such crafted attacks against ML-based AI systems come under the umbrella of a fascinating research field: adversarial machine learning. This attack against Face ID led to new debates in the field of cybersecurity: How secure are AI systems…

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