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) …
Call it Artificial Intelligence – Machine Learning – Algorithms, this automation shows that we can identify patterns in data and react to it. He describes how machine learning can be applied to the complex data sets generated by Digital Performance Management assets. While the Artificial Intelligence -- Machine Learning – Algorithms can automatically manage massive amounts of data, the benefits for businesses to deploy these technologies along with their Digital Performance Management assets is that they help reduce costs associated with problem identification and root cause analysis. Digital Performance Management is an example of a strategic set of highly complex real time data that businesses depend on.
Chinese internet giant Baidu and state-owned passenger vehicle manufacturer BAIC Motor Corp have formed a strategic partnership that will see the companies produce and promote autonomous vehicle technology in China. Announced on Friday at the Consumer Electronics Show (CES) 2017, Baidu's autonomous driving research and development arm, Baidu Intelligent Vehicle, will work with the automaker on two key projects, the first of which is the launch of a BAIC-built vehicle equipped with Baidu's telematics solutions at the Shanghai auto show in April. In September 2016, Baidu and Nvidia announced a partnership that combines Nvidia's self-driving computing platform with Baidu's cloud and mapping technology to develop an algorithm-based operating system capable of powering complex navigation systems in autonomous vehicles. In April, Baidu announced that it had formed a self-driving team in Silicon Valley, focused on the research, development, and testing of autonomous vehicles.
Before Siemens's current initiative of utilizing Watson in the industrial sector, the German conglomerate created a global strategic alliance with IBM to exploit the strength of Watson in healthcare. The ultimate aim of the pact between IBM and Siemens is to deliver automation in the industrial sector by allowing developers to build intelligent apps utilizing Watson's AI capabilities on Siemens's industrial cloud. However, lack of data-driven intelligent apps in the market results in wastage of data since benefits of IBM's cloud platform and analytics solutions can't be fully realized. Per my estimate, IBM's quarterly revenue from Watson (including revenues Watson indirectly drives) currently stands at somewhere below the $1 billion mark, which is about to cross $1 billion in the near-term due to the Siemens deal.
It can be tricky to predict trends, but job search site Indeed thinks it has the answer. Its team put together information from job postings and site searches going all the way back to 2014. IoT is following in a close second place and it's obvious why: Data gathered by IoT devices is best sorted and organized using machine learning techniques. There hasn't been any movement in the number of searches for cloud computing jobs--a good sign that the industry is saturated.
Though it's still early days for head-mounted virtual reality (NYSE:VR) and augmented reality (NYSE:AR) products, the interest and excitement about these types of devices is palpable. As a result, I expect revenues for virtual reality and augmented reality-based hardware devices (and accessories) will surpass revenues for the wearables market in 2017. Imagine a device, for example, that is a high-quality connected audio speaker, WiFi extender and smart speaker all in one. Not only will these ease the setup and reduce the physical requirements of multiple smart home products, they should provide the kind of additional capabilities that the smart home category needs to start appealing to a wider audience.
Many firms see big opportunity in IoT uses and enterprises start to believe that IoT holds the promise to enhance customer relationships and drive business growth by improving quality, productivity, and reliability on one side, and on the other side reducing costs, risk, and theft. All indications suggest that countless Internet of Things (IoT) devices that power everyday technology like closed-circuit cameras and smart-home devices were hijacked by the malware, and used against the servers. The technologies that have created the Internet of Things aren't changing the internet only, but rather change the things connected to the internet. The year 2017 would see Internet of Things software being distributed across cloud services, edge devices, and gateways.
Given that in general, algorithmic (and ML) approaches to extracting information could be more easily iterated into smaller chunks than the production of a robust statistical model, naturally industry was quick to adopt the idea. Someone who is sufficiently trained in statistics *should* be able to answer questions like that clearly, as statistical work includes developing an expertise in the subject under study. With the introduction of new data architectures (column based, streaming, batch/file-based, direct I/O) and the exponential increase in power of computer hardware (cloud computing, GPU computing, RAM speeds, solid-state storage, CPU capacity, etc. PMML helped create model portability, computing algorithms began to leverage GPU processing, multiple CPU threads, and manage memory better.
In the software development industry, 2016 was truly transformative. And we saw more software being deployed as a service in 2016, living on cloud servers with functionality that's just an API call away for developers looking to add certain elements to their software. The cloud began as "infrastructure as a service," then enabled "software as a service," and has become the platform for hosting these reusable assets. More than winning game shows or beating game champions, artificial intelligence represents the brains within these new systems, and 2016 saw an explosion of APIs that enable developers to build such functionality as natural language processing, personality insights and sentiment analysis into their software.
Craig Walker, CEO of business VoIP provider Dialpad, said digital disruption will spur enterprise leaders to adopt cloud-based solutions while legacy players consolidate or acquire next-generation providers. "Today's world requires analytics and operational capabilities to address customers, process claims, and interface to devices in real time at an individual level," added Schroeder. MapR's Schroeder said we'll see rapid adoption in 2017 in the form of relatively straightforward algorithms deployed on large data sets to address repetitive automated tasks. "AI is now back in mainstream discussions and [is] the umbrella buzzword for machine intelligence, machine learning, neural networks, and cognitive computing," said Schroeder.
These new cyber-physical manufacturing facilities use robotics, sensors, big data, automation, artificial intelligence, virtual reality, augmented reality, additive manufacturing, cybersecurity systems and other cutting-edge technologies to deliver unprecedented flexibility, precision and efficiency to the manufacturing process. IoT cloud platforms provide a powerful solution for harmonizing incompatible connected devices. It's useful to think of modern manufacturing environments as complex and interconnected living organisms, similar to the human body. One of the most significant impediments to realizing the Industry 4.0 vision is implementing the necessary tools and applications to holistically visualize operations, identify opportunities for improvement and implement changes.