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) …
"For example, when you type in a search for a job title, say with the phrase'job manager,' the LinkedIn engine will not only look for the title itself, but also people with relevant skills like time management, team coordination, risk assessment and so on," said Sakshi Jain, who is the Engineering Manager on LinkedIn's Responsible AI team. It can index and surface information on people from hundreds of sources as passive candidates typically aren't on job or career sites, and many people tend to only include piecemeal information on their LinkedIn and other profiles." "With the use of automated scheduling and email follow-ups, AI can help free up valuable time and solve the major pain point of extensive back-and-forth coordination with candidates and interviewers." "While there is no need to inspect every transaction or process run by the AI, there is a need to constantly review the performance of the AI steps to ensure that the outputs are in line with expectations. Tom (@ttaulli) is an advisor/board member to startups and the author of Artificial Intelligence Basics: A Non-Technical Introduction, The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems and Implementing AI Systems: Transform Your Business in 6 Steps.
In April, the number of job openings hit a record 9.3 million, according to data from the Labor Department. With the pandemic fading away, there has been a scramble to hire new employees and this has become a major challenge for companies. So then can AI (Artificial Intelligence) help out? The irony is that many companies are using the technology--and don't even realize it! The reason is that AI is built into the top online job sites. "For example, when you type in a search for a job title, say with the phrase'job manager,' the LinkedIn engine will not only look for the title itself, but also people with relevant skills like time management, team coordination, risk assessment and so on," said Sakshi Jain, who is the Engineering Manager on LinkedIn's Responsible AI team.
Sign text closeup for help wanted with red and white colors by entrance to store shop business ... [ ] building during corona virus covid 19 pandemic In April, the number of job openings hit a record 9.3 million, according to data from the Labor Department. With the pandemic fading away, there has been a scramble to hire new employees and this has become a major challenge for companies. So then can AI (Artificial Intelligence) help out? The irony is that many companies are using the technology--and don't even realize it! The reason is that AI is built into the top online job sites.
When sea snakes swim, they wind their way through the water by flicking their flattened tails, which is super graceful but requires a whole lot of coordination. So when roboticists at Carnegie Mellon University decided that it was time for their landlubbing robot snake to take to the water, they took a shortcut. They approximated the wildly complex biomechanics of a serpent--and then loaded the machine with propellers. The result is a sort of wiggling torpedo, sans warhead: the Hardened Underwater Modular Robot Snake. As you can see in the video below, it manages some impressive swimming by combining an aft thruster to produce forward movement with lateral thrusters along its body for stability control, plus it uses some bending joints (actuators, in the parlance) to position the lateral thrusters.
MIT researchers developed a picking robot that combines vision with radio frequency (RF) sensing to find and grasps objects, even if they're hidden from view. The technology could aid fulfilment in e-commerce warehouses. System uses penetrative radio frequency to pinpoint items, even when they're hidden from view. In recent years, robots have gained artificial vision, touch, and even smell. "Researchers have been giving robots human-like perception," says MIT Associate Professor Fadel Adib.
In recent years, robots have gained artificial vision, touch, and even smell. "Researchers have been giving robots human-like perception," says MIT Associate Professor Fadel Adib. In a new paper, Adib's team is pushing the technology a step further. "We're trying to give robots superhuman perception," he says. The researchers have developed a robot that uses radio waves, which can pass through walls, to sense occluded objects.
In recent past years, there have been dramatic improvements in AI underpinned by several technological advances. They will continue to take longer strides with even more developments and substantial progress in the coming years. As this happens, we are also creating (unknowingly) various risks to our socio-economic structure, civilization in general, and to some extent, for the human species. Species-level risks are not evident yet; however, the other two, socio-economic and civilization level risks, are significant enough to be ignored. From a business perspective, several risks could affect business metrics adversely. For now, let us talk about general outcome risks that can have a significant impact on critical social, civil, and business aspects.
Autonomous robots may be able to adapt their behavior in response to changes in the environment. This is useful, for example, to efficiently handle limited resources or to respond appropriately to unexpected events such as faults. The architecture of a self-adaptive robot is complex because it should include automatic mechanisms to dynamically configure the elements that control robot behaviors. To facilitate the construction of this type of architectures, it is useful to have general solutions in the form of software tools that may be applicable to different robotic systems. This paper presents an original algorithm to dynamically configure the control architecture, which is applicable to the development of self-adaptive autonomous robots. This algorithm uses a constraint-based configuration approach to decide which basic robot behaviors should be activated in response to both reactive and deliberative events. The algorithm uses specific search heuristics and initialization procedures to achieve the performance required by robotic systems. The solution has been implemented as a software development tool called Behavior Coordinator CBC (Constraint-Based Configuration), which is based on ROS and open source, available to the general public. This tool has been successfully used for building multiple applications of autonomous aerial robots.
The ability to communicate intention enables decentralized multi-agent robots to collaborate while performing physical tasks. In this work, we present spatial intention maps, a new intention representation for multi-agent vision-based deep reinforcement learning that improves coordination between decentralized mobile manipulators. In this representation, each agent's intention is provided to other agents, and rendered into an overhead 2D map aligned with visual observations. This synergizes with the recently proposed spatial action maps framework, in which state and action representations are spatially aligned, providing inductive biases that encourage emergent cooperative behaviors requiring spatial coordination, such as passing objects to each other or avoiding collisions. Experiments across a variety of multi-agent environments, including heterogeneous robot teams with different abilities (lifting, pushing, or throwing), show that incorporating spatial intention maps improves performance for different mobile manipulation tasks while significantly enhancing cooperative behaviors.
Effective communication is an important skill for enabling information exchange in multi-agent settings and emergent communication is now a vibrant field of research, with common settings involving discrete cheap-talk channels. Since, by definition, these settings involve arbitrary encoding of information, typically they do not allow for the learned protocols to generalize beyond training partners. In contrast, in this work, we present a novel problem setting and the Quasi-Equivalence Discovery (QED) algorithm that allows for zero-shot coordination (ZSC), i.e., discovering protocols that can generalize to independently trained agents. Real world problem settings often contain costly communication channels, e.g., robots have to physically move their limbs, and a non-uniform distribution over intents. We show that these two factors lead to unique optimal ZSC policies in referential games, where agents use the energy cost of the messages to communicate intent. Other-Play was recently introduced for learning optimal ZSC policies, but requires prior access to the symmetries of the problem. Instead, QED can iteratively discovers the symmetries in this setting and converges to the optimal ZSC policy.