5 Hurdles Facing Artificial Intelligence Growth

5 Hurdles Facing Artificial Intelligence Growth

Artificial intelligence has revolutionized information technology. The new economy of information technology has shaped the way we are living. Google led the way, showing the power of data-driven artificial intelligence delivered over the cloud, not only in search but also in tasks like language translation and computer vision. Artificial intelligence run through the cloud is now the dominant approach used by researchers at technology companies, universities and government labs.

We're seeing a rebirth of artificial intelligence driven by the cloud, huge amounts of data and the learning algorithms of software. Some predict that we are headed much further.

Artificial intelligences are now ubiquitous, from GPS navigation systems and Google algorithms to automated customer service and Apple’s Siri, to say nothing of Deep Blue and Watson — but no machine has met Turing’s standard. The quest to do so, however, and the lines of research inspired by the general challenge of modeling human thought, have profoundly influenced both computer and cognitive science.

It is important to remember that the benefits to Facebook and Google aren't hard to imagine. Rather than depending on users following brands, they could simply scan people's pictures and know whether they prefer Coke or Pepsi. Or, going a step further, the machine could determine your location, recognize you haven't posted a picture of yourself smiling in some time and recommend you buy tickets to the funny movie playing around the corner.

The following highlight application areas where AI technology is having a strong impact on industry and everyday life.

AI Application Areas

  • Aerospace
  • Bio-Informatics
  • Business Intelligence
  • Documentation and Layout
  • Emergency Response
  • Financial Advisory Systems
  • Homeland Security
  • Logistics and Supply Chains
  • Petroleum
  • Process Support and Workflow

AI Technology Areas

  • Knowledge-Based Systems: data mining, expert systems, knowledge management, KBS methodology, ontologies
  • Planning and Workflow Systems: modelling, task setting, planning, execution, monitoring and coordination of activities
  • Adaptive Systems: case-based reasoning: a technique for utilising past experiences and existing corporate resources such as databases to guide diagnosis and fault finding
  • Intelligent User Interfaces: intelligent agents, document presentation and argumentation, dynamic creation of content
  • Intelligent Virtual Worlds: collaborative workspaces, virtual operations centres, meeting assistants.

For more than 50 years, we’ve heard about the promise of artificial intelligence and intelligent machines, but most of the big success stories to date – the IBM Watsons of the world – have been the result of massive efforts by universities and corporate R&D labs rather than by emerging startups. That could change soon, as artificial intelligence shows signs of becoming the next big trend for tech startups in Silicon Valley.

Improving customer satisfaction by bringing down response times, fewer redundancies, a reduced time to market for new products and a more personalized approach is key. Artificial intelligence helps organizations improve customer interactions resulting in more loyal customers. IBM’s Watson, for example, has developed a financial services assistant that can provide better advice on financial products based on market conditions, life events, client’s past decisions and available offerings.

However, there are some shortfalls and hurdles to consider.

  1. One shortfall is the realization that the creation of a comprehensive AI solution such as IBM Watson – as amazing as it has been – may simply be too expensive to be economically viable over the long haul. Even IBM has been forced to admit that it needs to rethink how it does AI. The company wants Watson to eventually become a $10 billion a year business, but thus far, Watson has only been able to generate $100 million in new business. As a result, IBM is now talking about partnering with the entrepreneurial ecosystem to develop AI apps for Watson, the same way that developers partner with companies such as Apple to develop iOS apps.
  2. The second is a realization by companies such as Google and Facebook that they can use AI to solve smaller, real-world problems. AI doesn’t have to be able to beat a human at chess or win Jeopardy! – if it can produce better search queries or analyze your social graph, then that may be good enough. Facebook, with its DeepFace project, promises to solve the problem of facial recognition so that it can help with Facebook photos. New “deep learning” initiatives at Facebook can be used to make sense of your social graph.
  3. Google is without question one of the most innovative companies on the planet. It’s a company that is known mostly for its amazingly successful search and advertising businesses, and will probably be known for this for the foreseeable future. But lately, it’s also quickly becoming known for its rather unorthodox array of secondary business efforts. These efforts include things like driverless cars, wearable technology (Google Glass), human-like robotics, high-altitude Internet broadcasting balloons, contact lenses that monitor glucose in tears and even an effort to potentially solve death (difficult to believe, though).
  4. "The Internet of Things" has everyday devices equipped with sensors and connectivity to work together, understand what we’re doing and operate automatically to make our lives easier. And, of course, we’ll be able to control and configure it all, likely with our tablets and smartphones or by speaking. After all, Siri and Google Now have taken voice recognition mainstream. Smart devices use Internet technologies like Wi-Fi to communicate with each other, your laptop and sometimes directly with the cloud. Some also talk to a central hub that serves as control point for many different devices, like the Revolv. In the past, some of these devices were wired together into more complex systems. But it wasn’t until they were provided with some intelligence, connected to the Internet and empowered by a new wave of technological accessibility—through cloud computing, smartphones and the prototyping capabilities of digital fabrication—that the IoT came into being As new and challenging as today’s IoT is, it offers a large and wide-open playing field. The companies that gain the right to win in this sphere will be those that understand just how disruptive the IoT will be, and that create a value proposition to take advantage of the opportunities.
  5. Data analysis has risen as an intellectual force of its own, with implications for how we accept new knowledge as facts. In all of these exponentially-growing technologies— artificial intelligence, robotics, nanomaterials, biotech, bioinformatics, quantum computing, Internet of everything—these files that are going to transform everything we have. Digital technologies such as mobile, social media, smartphones, big data, predictive analytics and cloud, among others, are fundamentally different than the preceding IT-based technologies. Newer technologies touch the customers directly, and in that interaction, create a source of digital difference that matters to value and revenue. We call that source a digital edge.

Without a doubt, technology has been evolving to enable these AI advancements and other innovations for the last decade. The ubiquity of network connectivity and the proliferation of smart devices (such as sensors, signs, phones, tablets, lights and drones) have created platforms upon which every enterprise can innovate so long as potential hurdles such as these are top of mind.

Article written by Raj Kosaraju
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