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 healthcare sector, that contains a diverse array of industries with activities ranging from research to manufacturing to facilities management (pharma, medical equipment, healthcare facilities), generated in 2013 something like 153 exabytes (1 exabyte 1 billion gigabytes). It is estimated that by year 2020 the healthcare sector will generate 2,134 exabytes. To put that into perspective data centres globally will have enough space only for an estimated of 985 exabytes by 2020. Meaning that two and a half times this capacity would be required to house all the healthcare data. Big data have four V's volume, velocity (real time will be crucial for healthcare), variety and veracity (noise, abnormality, and biases). Poor data quality costs the US economy $ 3,1 trillion a year. And 1 in 3 business leaders don't trust the information they use to make decisions, and this is true also for the healthcare sector.
AI is seeping into enterprises from all directions. It's being embedded in applications, software tools, devices and equipment. Yet, some organizations still don't have an AI strategy. "The journey to AI having an impact at a firm is challenging and sometimes long," said Nigel Duffy, global artificial intelligence leader at professional services firm EY. "To have an impact with AI you must solve a lot of problems, many of which have nothing to do directly with AI, [such as] how do you deploy solutions? How do you get them into your infrastructure? How do you get people to use them? What are the workforce and change management considerations? What kind of training is required? AI is too disruptive to ignore. If your company lacks an AI strategy by design, then it has one by default. A default strategy is a Wild, Wild West scenario in which AI is popping up in various places within an organization, without orchestration and alignment. The lack of cohesion and direction can result in several issues including governance and security. "[S]ome of those risks may not be well characterized, so they have not been addressed by the appropriate level of governance and review," said Duffy. "Most AI is going to come through procurement or it's going to come through the backdoor or technology you've deployed." Should an AI strategy be part of a digital transformation strategy? One reason some people think an AI strategy should be part of a digital transformation strategy is because the digital transformation strategy is viewed as the overarching business initiative that's facilitated by modern technologies. "If an organization's digital transformation strategy does not already include AI, then there is a real need to revisit the overall approach to transformation, said David Homa, director of the Digital Initiative at Harvard Business School.
The auto industry is currently experiencing a rapid shift to autonomous vehicles (AV). This evolution is spearheaded by new, innovative technology companies that are bringing cutting-edge automotive platforms to the market at an unprecedented pace. Currently, vehicles on the road are equipped with the ability to maneuver on their own on highways while in the presence of a human driver. The next logical step in the race to autonomy is self-driving capability in an urban setting -- first with a driver and eventually with humans acting solely as passengers. However, driving in cities is an exponentially more difficult problem to solve than maneuvering on highways.
Recent advances in AI and ML, while not actually close to real AGI, have made a feeling that AGI is close, as surprisingly fast for many years. Artificial Intelligence is something that's been around quite a while. Since its development into the public consciousness through sci-fi, many have expected that one day machines will have "general intelligence", and considered diverse practical, ethical and philosophical implications. In all actuality, AI has been the discussion of standard pop-culture and sci-fi since the first Terminator film turned out in 1984. These motion pictures present an example of something many refer to as "Artificial General Intelligence".
As banks and credit unions pivot from managing the impact of the pandemic to reopening and repositioning business models to reflect a more digital economy, it is clear many of the changes in consumer behavior will be altered forever. From the way consumers shop for new financial services to the way they transact and interact, we are beginning to understand that consumers are expecting digital experiences to be central to all stages of their customer journey. But digital capabilities and improved customer experiences don't operate in a vacuum. In a digitally empowered world, financial institutions must leverage the power of big data, AI and machine learning to drive customer engagement and conversion. To accomplish this, many institutions are moving to the cloud, hiring data scientists and officers, and finding marketers who understand how to bridge the gap between the pace at which data is generated and the ability to create real-time engagement.
When we think about agriculture, we tend to think about old-school farming. But although many of us might think that the agricultural community is behind the curve when it comes to implementing new technologies, there is lots of evidence that farmers are actually moving quite quickly to modernize almost everything about the farming process -- they're using artificial intelligence in new and amazing ways to bring the process of food cultivation into the future. High-tech agriculture starts at the very second that the seed is first placed in the ground. Experts in the field are familiar with "variable rate planting equipment" that does more than just planting a seed down into the dirt somewhere. As you'll see later in this article, all sorts of artificial intelligence work is being done behind the scenes on predictions -- where a seed will grow best, what soil conditions are likely to be, etc.
Welcome to the fifth entry on a series on Reinforcement Learning. In the previous article, we presented the MDP Framework for describing complex environments. This allowed us to create a more robust and diverse scenario for the basic Multi-Armed Bandits problem, which we called the Casinos Environment. We then implemented this scenario using OpenAI's gym, and made a simple agent that acted randomly to showcase how an interaction is realized under the MDP Framework. Today, we're going to focus back on the agents, and show a way in which we can describe an agent's behavior in complex scenarios, where past actions determine future rewards.
It is the time of the fall classic, Major League Baseball's World Series. As the two best teams vie for the championship this year, there are some actors in the game beyond the players, coaches, umpires (or referees), and fans… namely big data, analytics, and artificial intelligence. These new actors are also highly prevalent in football, basketball, and hockey, and they are changing these games forever. Sports foray into technology and data really got its start in 2002 with the Oakland Athletics. General Manager Billy Beane and Assistant GM Paul DePodesta would pioneer sabermetrics, which is a new perspective on baseball analytics.
Big data refers to incredibly broad collections of structured and unstructured data that can not be managed using conventional methods. Big data research can make sense of data by uncovering trends and patterns. Machine learning can accelerate this process by decision-making algorithms. It can categorize incoming data, identify trends, and convert data into insights that are useful for business operations. Machine learning algorithms are useful for gathering, analyzing, and incorporating data for large organizations.