Where Machine Learning Meets Artificial Intelligence
The field of artificial intelligence (AI) is one of the most dynamic and disruptive technological revolutions today. This ever-evolving phenomenon gave birth to machine learning, also popularly referred to as ‘Machine Intelligence’ or ‘Deep Learning’. Machine learning enables machines to learn how to perform tasks without any programming. As technology and insights on how the human mind works progress, our concept of what constitutes AI and machine learning is also constantly changing.
AI has empowered organizations to create, strategize, and innovate at an entirely new level, driving growth and productivity. By eliminating repetitive tasks, AI has trained machines and allowed the workforce to instead focus on value analysis, decision-making, and innovation. Inventions such as Amazon’s Alexa and IBM’s Watson have leveraged the power of the cloud, machine learning, and artificial intelligence to facilitate a series of meaningful breakthroughs in today’s tech world. We are on a path to witnessing superintelligent machines across several sectors like never before.
Three major breakthroughs spearheaded machine learning as a powerful vehicle driving AI development forward. In 1959, Arthur Samuel proposed that rather than programming information to computers and showing them how to carry out tasks, it might actually be possible to teach them how to learn for themselves.
The second recent major breakthrough was enabled by the emergence of the internet. The huge amounts of data generated, stored, and made available for distribution were programmed into machines to read into trends and generate analytic reports through real world interactions.
The third development, Neural Networks (computer systems modeled on the human brain) have played a huge role in teaching computers to think and understand the world, while still retaining the core advantages that they hold over us such as speed, accuracy, and lack of bias. Neural Networks are computer systems which operate by segmenting information very similar to a human brain. These systems are trained to recognize items based on their images, voices, characteristics, data, and more and classify them according to the elements they contain.
AI and machine learning continue to shape how several industries are approaching business by constantly inventing revolutionary new technologies. Some of the areas where AI and machine learning are driving impact include:
Enhanced Corporate Security and Reliability
Since security and loss of data are major concerns for most enterprises, some storage vendors have begun to harness AI and machine learning to prevent data loss, increase availability, and speed turnaround during downtime via smart data recovery and systematic backup strategies. AI enables smart security features to detect data loss during transit.
Perhaps the biggest beneficiaries of AI and machine learning integration will be drivers. AI techniques have been very useful in the automobile industry as it has aided in setting up advanced driving assistance systems, GPS systems, autonomous driving capabilities, infotainment systems, performance data, smart sensors, and many more. Machine learning has also played a huge role in emerging cognitive vehicle systems within the auto industry.
Several industries ranging from transportation, defense, and facility operations rely on machine learning to fulfill tasks. However, the most prevalent area is undoubtedly manufacturing. With many labor intensive and highly repetitive tasks, the use of automated machinery has rapidly improved efficiency and created greater quality control of outputs. AI has also enabled automated storage facilities to adopt agile and flexible architectures thus saving human labor and improving efficiency.
Hybrid Storage Clouds
AI and machine learning have accelerated fluid hybrid cloud solutions deployment since data is analyzed while logic maps are quickly developed to flow transparently to local analytics engines – bringing about continuous improvements. Artificial intelligence facilitates software architectures which transition data seamlessly from one type of cloud to another. At the same time, organizations can manage all their data as one pool, regardless of where it physically resides.
In today’s world, software engineers usually write programs (code) which instruct computers to perform various tasks. With numerous efforts invested in machine intelligence, these days are numbered. In fact, in years to come, machines will be teaching other machines how to think and operate tasks. The future of the workforce will be dramatically impacted by machines. At this point, will intelligent machines replace us, co-exist with us, or merge with us?
Developing applications which don’t embrace AI and machine learning methodologies either through verification, validity, security or control is no longer an option. At the rate which AI is taking off, I definitely can’t wait to witness the machine take-over.
At Extentia, artificial intelligence and machine learning play an integral role in shaping our enterprise mobility strategies and agility efforts. At least twice a month, we host Techquarium – a platform whereby we get to showcase the latest technological innovations to our audiences.
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