Leading software development companies, particularly in India are Microsoft Gold Certified software development organizations that focus on timely, highly qualitative and cost-effective IT outsourcing services to the clientele. One particular company, TatvaSoft has a varied and rich experience and stringent quality standards that ensure that the software developed could provide a business an edge over the competition. With the amazing rise of AI technology, the service provider aims to keep up with the changing trends and changing demands of the customers.
These days, everyone is talking about deep learning, machine learning and AI. 2017 witnessed an explosion of machine learning in production use, with even artificial intelligence and deep learning being leveraged for practical apps. Basic analytics are out, machine learning are in.
FIVE ARTIFICIAL INTELLIGENCE TRENDS THAT WILL DOMINATE THIS YEAR
Check out the trends in the AI space that are set to dominate this year.
- Organizations would operationalize artificial intelligence. Whether people recognize it or not, AI is already here. A lot of enterprises are already using AI, but may not refer to it as AI. For instance, a company using a chatbot feature in engaging customers is using AI. However, a lot of the deployments that leverage AI tools and technologies have been small-scale. Expect enterprise to ramp up in a huge way this year. Organizations have spent the past few years educating themselves on different AI tools and frameworks. However, as AI becomes mainstream, it would move beyond small-scale experiments to be operationalized and automated.
As companies move forward with operationalizing artificial intelligence, they would look for tools and products for automating, managing and streamlining the whole deep learning and machine learning life cycle. It is predicted that 2018 would see a boost in the investment of AI life cycle management, as well as technologies that house data and supervising the process would mature.
- Bias in training sets of data would continue troubling AI. Companies should get their data in order. It’s believed that debate regarding data sets would take center stage this year. Everywhere, enterprises are adding AI to their products, to make them more efficient, smarter and even autonomous. In 2017, there were competing arguments whether AI will build jobs or eliminate them, some even proposing the end of the human race. What emerged as a major part of the conversation is how training sets of data shape the behavior of the models. It turns out that models are only as good as the training data they utilize and the development of an effective, representative data set is very challenging.
- Artificial intelligence reality would lag the hype once again. There have been repeated predictions for years that tout possible breakthroughs in using AI as well as machine learning, but the truth is that most organizations have yet to see quantifiable benefits from their investments in these fields. The hype today has been overblown, and a lot of companies are hesitant to begin, due to a combination of lack of expertise, skepticism and most of all, the lack of confidence in the reliability of the sets of data. As a matter of fact, while headlines would be mostly regarding AI, most companies would have to focus first on IA or information augmentation. Getting the data organized in a way that ensures it could be reconciled, could be refined and related to discover important insights, which support efficient business execution across all departments, while at the same time addressing the regulatory compliance burden. This year would see a backlash against the AI hype, but a more balanced approach of deep and shallow learning app to business opportunities would emerge as a result. Although there may be a backlash against the hype, it will not hinder big companies from investing in AI as well as other related technologies.
- Cloud adoption would accelerate to support innovation of AI. Organizations this year would seek to boost their processes and infrastructure to support AI and machine learning efforts. As enterprises look to boost and innovate with AI and machine learning, more specialized infrastructure and tooling would be adopted in the cloud to support particular use scenarios, such as solutions to merge multi-modal sensory inputs for human interaction, like for instance, touch, think sound and vision or solutions to merge satellite imager with financial data to catapult algorithmic trading capabilities. There would be an explosion in cloud-based solutions, which accelerate the present pace of collecting data and demonstrate further the need for on-demand, frictionless compute and storage from providers of managed cloud.
- AI should solve the ‘black box’ issue with audit trails. One of the huge hindrances to AI adoption, especially in regulated industries, is the difficulty in showing exactly how an artificial intelligence reached a decision. AI is increasingly being used by app developers and apps such as drug discovery or a connected car, and these apps could have a detrimental effect on human life if incorrect decision is made. Exact detection of what caused the final incorrect decision that lead to a serious problem is something that companies would begin to look at this year. Tracking and auditing each input and every score, which a framework produces would help detect the human-written code, which ultimately caused the issue.
The AI technology already is taking a bigger role in user experience across different interface. Consider people’s conversations with chat bots on the web, mobile phone queries to Google Now and the requests to Alexa speaker at home. People are interacting with technology via an AI intermediator. AI now is considered as the new UI and has quickly become more than just an underlying technology capability. It would permeate intelligent organizations and advance to a fundamental tool for day-to0day engagement with people, customers and employees alike. Software companies are leveraging the AI trend to stay on a competitive edge.