The Need For Data Infrastructure For Best Utilization Of Artificial Intelligence
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Technology is rapidly transforming our world into a ‘smart’ world with innovations such as smartphones, smart cars, smart homes, and so much more. The Internet of Things (IoT) is one of the major contributors here. At its core, IoT is all about connected devices equipped with sensors and microchips that help transmit data via the Internet over a centralized platform for enhanced efficiency and performance. Estimates suggest that by 2020, the number of connected devices all over the world will be nearly 50 billion.

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As the number of connected devices continues to escalate rapidly, there will arise the need to manage and analyze the sheer amount of data generated by these devices. This data is crucial as it holds valuable insights and patterns that can help businesses and organizations to assess potential risks, find new business opportunities, and most importantly improve the overall functioning and productivity of organizations. Now, this is where Artificial Intelligence (AI) and Machine Learning (ML) come in. AI and ML technologies allow data scientists to delve deep into massive volumes of data and uncover meaningful insights from it. Stressing the importance of AI for IoT and machines, Prof. John McCarthy aptly stated:

“The goal of AI is to develop machines that behave as though they were intelligent.”

Although at a developing phase, AI has already started creating job prospects in the global market. Capgemini reports that nearly 83% of organizations leveraging AI technologies maintain that new jobs have begun to crop up in the field of AI. With more and more organizations joining the bandwagon of Big Data and AI, there’s now an enormous demand for skilled data professionals such as data scientists, data engineers, data analysts, and much more.

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Getting your career started in AI has become much more convenient now that the internet is swarming with highly educative and informative online Artificial Intelligence courses. These courses are specially tailored to introduce aspiring students to the basic concepts of AI such as algorithms, natural language processing, etc., and gradually progress towards the more complex concepts such as Machine Learning and Deep Learning. If you’re even remotely related to the tech-scene, it’s recommended you go through these courses and improve your employability. Because, as it must be clear by now, organizations far and wide are constantly on a lookout for up-skilled data professionals. There couldn’t be a better time for you to start than now!

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Role Of Computation In Management Of Data

Today, data is exponentially increasing — nearly 2.5 quintillions of data is generated daily! In a data-driven world that we live in, this data is the source of all information. However, managing such massive volumes of data is a challenge in itself. Thanks to high-end computing processes we’re able to store, process, and analyze this data to reveal the hidden trends and insights that can enhance business operations, improve the quality of life, and most importantly, foster innovation.

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Computers can process and analyze data much more quickly and accurately than humans and hence, are immensely helping companies leverage the data at their disposal to calculate vital statistics, monitor employee performance, power automation, boost productivity and sales, improve the efficiency of machines, and much more. It is because of this computation power that organizations today have access to an extensive range of statistical and technical insights and figures. Furthermore, technologies such as cloud computing have enhanced the functionality of AI. For instance, industrial magnates like Netflix and Amazon rely on cloud-based frameworks to process AI tasks and services that demand comprehensive analysis and computing.

Data Infrastructure and Artificial Intelligence

Data infrastructure is an essential aspect of data processing and analysis. Businesses and organizations cannot create value out of data without having the proper data infrastructures. By data infrastructure, we mean the entire backend computing support system required to process, store, transfer, and safeguard data.

While many companies use a software-defined infrastructure (SDI) to support the dynamic IT environment, SDI has its backlogs. Not only is SDI constrained by the static source code, but its functioning also largely depends on the skills and expertise of the software developer who writes the codes for a particular environment. Thus, SDI is not ‘intelligent.’ The trend is now shifting towards Artificial Intelligence defined infrastructures (ADI).

Essentially, ADI is an intelligent upgrade of SDI, fully stacked with sophisticated AI and ML algorithms that can ‘learn’ from the existing information to automatically build self-healing frameworks. ADI can:

Constantly analyze the dynamic behavior of all the components of the existing infrastructure and ‘learn’ to understand their working by itself.

Create an error-free environment by proactively monitoring the functioning of the components of the infrastructure and autonomously taking actions to fix issues, if any.

Deploy resources according to the requirements of the workload and also de-allocate particular resources when they are no longer required.

These data infrastructures help support various AI-related technologies and application, including:

Support for existing frameworks : A data infrastructure should incorporate AI frameworks like TensorFlow, Caffe, Theano, and Torch to enhance and support the present framework. However, the focus should not rely solely on AI frameworks but it should be such that it can fit the requirements and interests of software developers.

: A data infrastructure should incorporate AI frameworks like TensorFlow, Caffe, Theano, and Torch to enhance and support the present framework. However, the focus should not rely solely on AI frameworks but it should be such that it can fit the requirements and interests of software developers. GPU-friendly environment : To ensure the seamless processing of AI tasks and processes, data infrastructure must support GPU environments having impressive computational power. Microsoft’s N-series GPU instances is an excellent example of this

: To ensure the seamless processing of AI tasks and processes, data infrastructure must support GPU environments having impressive computational power. Microsoft’s N-series GPU instances is an excellent example of this AI-optimized management environment and tools : The lack of proper management environment and tools for supporting and running AI applications is one of the most significant drawbacks of the present data infrastructures. Hence, it is essential to integrate the right management tools and environment to deliver optimal performance.

: The lack of proper management environment and tools for supporting and running AI applications is one of the most significant drawbacks of the present data infrastructures. Hence, it is essential to integrate the right management tools and environment to deliver optimal performance. Machine Reasoning: Machine Reasoning in an integral part of AI environment. Machine reasoning algorithms allow AI environment to keep up with the dynamic trends of infrastructures and technologies, thereby providing meaningful insights into the best course of action.

Importance Of Organizational Skills In The Implementation Of AI

Merely investing in and leveraging AI technologies isn’t enough. Companies and businesses require a talent pool of professionals who can employ these AI tools in a way that can ensure the best possible results. As artificial intelligence and machine learning continue to penetrate industries and businesses across the globe, there’s an increasing need to rethink the entire leadership and thought-process of an organization, right from product strategy and customer experience to finding ways to boost the productivity of human capital. Leaders of the business world have to encourage intelligent AI integration within their system by:

Putting People First

Investing in AI technologies alone will reap no results. Businesses have to invest in their employees as well proportionately. According to Missy Cummings, Director of Humans & Autonomy Lab, Duke University,

“The future of AI is supporting and augmenting human capabilities such that a human-AI team is stronger than either alone.”

Cultivating A Work Culture Of Learning

Being a developing field, AI demands constant training and learning to keep up with the innovations and changes it is bringing about today. In the future, AI will bring in new changes in the field of technology and only by encouraging and creating a work environment of constant learning and training can organizations keep up with the innovations of AI.

Foster Transparency In Business Operations

Organizations should strive to maintain and push for transparency in all aspects of their business. Leaders and managers should engage in open communication with employees about their approaches to AI. This will not only help gain their trust and confidence but will also boost their morale and motivate them to work harder.

Alain Dehaze, CEO of The Adecco Group, maintains:

“Augmented intelligence, blending human input and AI, will be the critical driver to succeed in business and to form a prosperous society. We are on the cusp of technological change unlike any we have seen before in human history. Technological advancement is requiring a deeper focus on innately human skills — critical thinking, emotional intelligence and value judgments.”

Having the right data infrastructure along with the proper set of organizational skills can thus, help businesses and organizations leverage artificial intelligence tools and technologies in the right way.
['Vivek Kumar']
['Artificial Intelligence', 'IoT', 'AI', 'Data']