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) …
When we look at all of the different types of HR technologies that are out there, and there's now hundreds of them that have some form of machine learning or AI at the core of their platform, and have an HR use case. One of the biggest areas that we're seeing an impact is in recruitment. Over a third of all of the vendors that are out there that are using some form of AI in their platform are doing so to try and disrupt recruitment and there are a few different ways that they're doing that, these include: To learn more about how AI is impacting Recruitment, you can read an earlier blog post that I wrote on this topic by going here. So I'm not talking about employee engagement or engagement surveys when I talk about Employee Experience. What I'm talking about is the experience that people have in your organisation using HR technology.
Machine learning (ML) has taken the industry by storm as hardware acceleration and applicable use cases have grown. ML can benefit from hardware, but that's not necessarily a requirement. Its popularity among developers has opened up opportunities using platforms like a Raspberry Pi 3. Deep neural networks (DNNs) are at the heart of many ML models. Such models can be used for a range of applications from classification to detection, and from adaptive driver-assistance systems (ADAS) to projecting failures of motors in the field. The challenge is that all ML models are not the same.
If you're one among the people of Generation X (born sometime between the 1960s- 1980s), you might be one among those complaining about the rapid expansion of technology, and how its complexities sometimes don't make sense. For millennials, the present world is an interesting place to be. New tech just keeps popping out of somewhere time and time again. Their short attention spans are satiated by the presence of myriad gadgets to try, a huge set of social media apps to socialize and an adaptive capability to catch up with nuances of new & emerging tech. But not all of us are tech nerds, and most of them include your parents, older relatives, and neighbors.
In the last 10 years, we've seen some significant breakthroughs in the domain of artificial intelligence (AI) and machine learning. In 2011, IBM Watson showed the world that it can be a reality TV show winner. In 2014, Google acquired an AI company called DeepMind, and one of its project, AlphaGo, beat the European Go champion in 2015. In 2016, Google made its TensorFlow library open source, which made machine learning accessible to the masses. Last year, people were left dumbfounded when Google Duplex made a haircut appointment over the phone.
Wave Computing, the Silicon Valley company accelerating artificial intelligence (AI) from the datacenter to the edge, announced its new TritonAI 64 platform, which integrates a triad of powerful technologies into a single, future-proof intellectual property (IP) licensable solution. Wave's TritonAI 64 platform delivers 8-to-32-bit integer-based support for high-performance AI inferencing at the edge now, with bfloat16 and 32-bit floating point-based support for edge training in the future. Wave's TritonAI 64 platform is an industry-first solution, enabling customers the ability to address a broad range of AI use cases with a single platform. The platform delivers efficient edge inferencing and training performance to support today's AI algorithms, while providing customers with flexibility to future-proof their investment for emerging AI algorithms. Features of the TritonAI 64 platform include a leading-edge MIPS 64-bit SIMD engine that is integrated with Wave's unique approach to dataflow and tensor-based configurable technology.
When we think of artificial intelligence (AI) and machine learning (ML), we tend to think of a technology that is new and at the cutting edge. In reality, AI and ML have been around since the 1950s and 1960s. The concept of the technology hasn't changed; what's evolved is the technology that makes AI and ML easier to use and applicable to more industries. The companies that are further along in their innovation journeys, those identified as digerati and digital experimenters, have already mastered the foundational technologies. AI and ML are becoming a tool that smart companies are using to innovate on the foundation they have already put in place.
The explosion of emerging technologies such as artificial intelligence (AI) is dramatically changing the way businesses operate today. As businesses collect more and more data, the need for solutions to drive true value from that data grows in importance. AI, in conjunction with big data and analytics, can deliver that baseline value and go beyond traditional solutions to find deeper insights. In India, banks are fast moving in this direction and deploying AI-powered chatbots for their operations to gain better insights into their customers' usage patterns, offer customised products, help in detecting fraudulent transactions and improving operational efficiency amongst others. There is no denying that AI helps banks nurture their relationships through better interactions with their customers however, not without challenges.
Chatbots are noticeably one of the most popular AI technologies across the world. From simplifying business workflows, enhancing employee and customer experience to reducing costs, chatbots provide numerous benefits for organizations of all sizes. The use cases of chatbots are diverse and vary across departments and industries. Most enterprise leaders are yet to understand and explore the full potential of chatbots.
Israeli radiology startup Aidoc has received FDA clearance for its AI-based product meant to help identify potential cases of pulmonary embolism in chest CT scans. Pulmonary embolism (PE) – which occurs when a blood clot gets lodged in the lung – is considered a silent killer that causes up to 200,000 deaths a year in the United States. The condition often strikes with little to no warning and diagnosis of a case can be extremely time-sensitive. Aidoc's technology doesn't require dedicated hardware and runs continuously on hospital systems, automatically ingesting radiological images. The 70-person company focuses on workflow optimization in radiology to help triage high risk patients for additional and faster review.
RPA promises to run 24/7 with no stops or breaks, minimizing human errors along the way. While RPA has several use cases including billing management, customer onboarding, data validation, customer service inquiry routing, inventory list updating, loan qualification and risk assessment, a lot of it still requires human intervention. This is where newer technologies are promising to take automation to the next level. In recent times, RPA's adoption as an automation approach has become mainstream and many companies have started looking at RPA as one of their key strategic investment areas. While the expectation is high on the RPA initiatives, about 30-50 % of the initial RPA projects fail due to existing siloes.