The problem of learning and decision-making is at the core of human and artificial thought, which is why scientists introduced machine learning (ML) into artificial intelligence (AI).
AI is a platform or solution that appears to be intelligent and can often exceed the performance of humans.
It is a broad description of any device that mimics human or intellectual functions, such as mechanical movement, reasoning or problem solving. ML is a widely used AI concept that teaches machines to detect different patterns and adapt to new circumstances and can be both experience- and explanation-based.
For instance, in robotics, ML plays a vital role by optimizing machine-based decision-making, which eventually increases a machine’s efficiency by enabling a more organized way of performing a particular task.
According to Andrew Ng, an AI pioneer, 99% of the economic value created by AI comes from supervised learning systems.
Nowadays, ML is used in many applications and is a core concept for intelligent systems, leading to the introduction of innovative technologies and more advanced concepts of artificial thinking.
It is a statistical- and data-driven approach to creating AI, such as when a computer program learns from data to improve its performance in completing a task like voice recognition.
As a result, ML systems are dependent on accessing large amounts of data in real time. In addition, creating the data and the quality of the data being accessed is often vital to the success of the machine.
For example, according to Ng, leading speech recognition applications can understand what someone is saying, although they may require 50,000 hours of speech and their transcripts to do so.
AI-based technologies are gaining mindshare among corporate enterprises around the world consider IBM’s Watson, Google’s DeepMind and AWS’s multiple AI services that took center stage at last month’s AWS re:Invent.
In our 2018 predictions for IT blog article, we discussed how cognitive and AI systems are finally becoming more mainstream, with IDC forecasting their worldwide revenue reaching $12.0 billion this year.
Core AI technologies in addition to ML, include technologies such as deep learning, natural language processing (NLP) and computer vision.
These all provide enhanced functionality to computers that is similar to human output, such as pattern recognition, analytical decision-making and predictions based on acquired data.
Incorporating AI and machine learning capabilities in enterprise software automates employees’ everyday tasks and enables them to invest their time performing higher value assignments.
Deloitte has predicted that over 80% of the largest enterprise software companies will integrate AI functionality into their products by the end of this year, and expects that by 2020, 95% of the top 100 enterprise software companies will have AI-enabled apps.
AI applications have changed the way we use computing services and every aspect of our computing behavior has been influenced by machine-learning algorithms that remember everything from our choices in music subscription services, to our desired merchandise in online shopping.
Businesses can also leverage AI to predict system failures by recognizing the patterns in which they occur through AI-based business intelligence software.
This type of application can be an AI program that monitors business activities and alerts businesses when and where a problem arises.
Similar use cases are also applicable in online security, where firewalls and intruder detection systems have been enhanced by machine learning and the pattern recognition capabilities of AI-based firewalls.
For example, our customer, Zenedge is a leading provider of cloud-based, AI-driven web application firewall (WAF), malicious bot detection and DDoS cybersecurity solutions.
It’s leveraging an interconnection-first approach on Platform Equinix™ to build a global network across the U.S., Canada, Europe and Asia to accommodate its unprecedented digital business growth.
AI continues to push enterprise computing to achieve superhuman capabilities. The processing of large amounts of data in parallel computing, while using big data for pattern recognition or performing real-time tasks, requires on-demand communications between AI systems and data.
And as more data is being produced, enterprises are accessing AI-based cloud computing systems that are capable of processing data in larger amounts, needing greater interconnection between the data, analytics and AI systems.
Many organizations struggle to make sense of the enormous amount of data they encounter every day, including preferences, purchases and other personal information collected from customers.
And now, the Internet of Things (IoT) is producing a big “haystack” of data in which enterprises need to find useable information and insights (the proverbial “needle”).
By applying the analytic capabilities of AI to data collected by the IoT, companies can identify and understand patterns and make more informed business decisions.
This leads to a variety of benefits for both companies and their customers such as proactive intervention, intelligent automation, and highly personalized shopping experiences. It also enables us to find ways for connected devices to work better together and make these systems easier to use.
While IoT is impressive, it doesn’t amount to much without a good AI system and on-demand access to it. Both AI and IoT technologies need to reach the same level of development and a higher level of private interconnection in order to function as perfectly as possible. Integrating AI into IoT networks is becoming a prerequisite for success in today’s IoT-based digital ecosystems.
So businesses must move rapidly to identify how they’ll drive value from combining AI, IoT and interconnection—or face playing catch-up in years to come.
Critical to the success of AI and machine learning systems is the direct and secure interconnection among a web of systems, users, applications, analytics, data and things.
Fast, private interconnection between systems or humans and systems can actually mimic the real-time interaction between humans (teacher and student) required for greater learning.
At Equinix, we believe that the ability to directly and securely interconnect companies and privately exchange data is the way of the future for all businesses. This especially pertains to those companies that need to leverage AI, ML and the IoT to be viable in today’s global digital economy.
Our Equinix Cloud Exchange (ECX) Fabric facilitates proximate interconnection in more than 48 major metros around the world, including out at the digital edge, where commerce, population centers and a growing number of business ecosystems and IoT devices meet.
The ECX Fabric allows private connection between businesses inside Equinix International Business Exchange (IBX) data centers in North America and EMEA, and, eventually, every Equinix IBX data center in the world.
For example, in the oil and gas industry, we are helping energy companies and their cloud partners use AI, ML and the cloud to remotely monitor IoT sensors on distributed oil wells to diagnose potential safety issues.
In the future, we expect that the AI/ML trend will continue in all industries as more companies collaborate and leverage integrated interconnection hubs and colocation data centers as a unifying, worldwide platform for innovation.
Article by Monalisa Bandopadhaya and Loveneesh Bansal, Equinix Blog Network