If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The increasingly digital economy requires boards and executives to have a solid understanding of the rapidly changing digital landscape. Naturally, artificial intelligence (AI) is an important stakeholder. Those organisations that want to prepare for an automated future should have a thorough understanding of AI. However, AI is an umbrella term that covers multiple disciplines, each affecting the business in a slightly different way. Artificial intelligence consists of the seamless integration of robotics, cognitive systems and machine learning.
On one hand, organizations recognize the potential value of machine learning to scale operations, gain faster and deeper insights, respond to quickly changing conditions, and more. On the other hand, it's hard to get started on something that is novel to your organization. You may not have the talent in-house, and you don't have any experience. What's more, even for those organizations that have run successful pilots, many have struggled to move those pilots into production for a variety of reasons. It feels like many organizations are stuck.
The perception that self-driving cars can really operate themselves without driver involvement is worrying automotive watchdogs, who say that some Americans have grown dangerously confident in the capabilities of semi-autonomous vehicles. Their comments come as electric vehicle maker Tesla's so-called Autopilot system is under scrutiny once again following a crash that killed two passengers in the Houston area late Saturday. "I would start by saying there are no self-driving cars despite what you may read about or what you've seen advertised," said Jake Fisher, senior director of auto testing for Consumer Reports. "And there's certainly nothing anywhere close to self-driving that is in production right now." Tesla has been the most common target of critics for marketing that its vehicles are capable of "full self-driving" with an upgrade. They are not capable of full self-driving – and, in fact, Tesla says on its website that drivers are supposed to keep their hands on the wheel at all times, ready to take over when the system is not able to steer, accelerate or brake on its own.
The online landscape is burgeoning. It is not just about computers, notebooks, tablet computers, and tablets. Presently a great number of apparatus are internet-connected. The listing of "smart" apparatus comprises washing machines, robotic vacuum cleaners, door locks, toys, and even toasters. The Internet of Things is the umbrella word -- and, yes, now you can purchase a wide umbrella -- for whatever connects to the web.
These days, companies are using cloud services to receive and process the data they gather from sensors, cameras, and services. However, the amount of data is getting so massive that sending them and managing them is becoming increasingly expansive. This is where Edge AI comes in, a combination of Edge Computing and Artificial Intelligence. Edge AI is a system of AI-equipped chips that are on board multiple devices. These devices can be installed and set up much closer to the sources of data. Although these chips process with less processing power and maybe slower action, they can provide invaluable services in terms of receiving and processing the data.
Artificial Intelligence is one of the most, if not the only disruptive technology that made a massive impact in the modern world. It is a concept that continues to reach a wider audience with regular developments and researches done by scientists, engineers, and entrepreneurs who are working to advance the field. Before the pandemic wreaked havoc in 2020, machine learning, a branch of artificial intelligence was causing disruptions across industries. But during the COVID-19 pandemic, it became evident that self-teaching algorithms and smart machines will play a big role in the ongoing fight against the viral outbreak and serve our society in the future too. Artificial intelligence technology remains a key trend in our work world and personal world.
What is Augmented Intelligence and why should you care about it? The AI market is projected to grow to $190Billion by 2025. AI is being used in every industry and is projected to be a core skill for the future. So why is there a new AI? Augmented intelligence refers to the idea that humans and artificial intelligence combined can create better results than either alone.
Artificial intelligence, in the form of machine learning, has the potential to transform many safety-critical applications such as those in transportation and healthcare. However, despite significant investment and impressive demonstrations, such technologies have struggled to live up to their promises. To this end, this article illustrates that machine learning fundamentally lacks the ability to leverage top-down reasoning, a critical element in safety-critical systems. This is especially important in situations where uncertainty can grow very quickly, requiring adaption to unknowns. This fundamental lack of contextual reasoning, combined with a lack of understanding of what constitutes maturity in artificial intelligence-embedded systems, has significantly contributed to the failures of these systems.
Recommender systems are among today's most successful application areas of artificial intelligence. However, in the recommender systems research community, we have fallen prey to a McNamara fallacy to a worrying extent: In the majority of our research efforts, we rely almost exclusively on computational measures such as prediction accuracy, which are easier to make than applying other evaluation methods. However, it remains unclear whether small improvements in terms of such computational measures matter greatly and whether they lead us to better systems in practice. A paradigm shift in terms of our research culture and goals is therefore needed. We can no longer focus exclusively on abstract computational measures but must direct our attention to research questions that are more relevant and have more impact in the real world. In this work, we review the various ways of how recommender systems may create value; how they, positively or negatively, impact consumers, businesses, and the society; and how we can measure the resulting effects.