2020 has been a hectic and an unprecedented year. Right from the eccentric rise in COVID cases to markets striking rock bottom, it will remain a fictional memoir for all of us. Yet we cannot rule-out the silver lining where the digital market has been forced-forwarded to the ‘new normal’ age. Experts comprehend it as a timeline shift, and to the fantasy of coders, Artificial Intelligence and Machine Learning (AI-ML) is taking center stage as new tech initiatives to look up for.
Thanks to the virtual uptick to cope with both education and administration today that has facilitated the scope for AI to play its part amongst growing demand. According to a recent survey by McKinsey firms, the potential for AI has value creation is immense across industries. It is also stated that AI will unlock USD 1 trillion of incremental value for the banking sector itself.
If you are thinking how AI-ML is creating such wonders, then let me list here the AI advantages –
- Excellent to boost revenues – an average of 38% by 2035
- Entails possibilities for hyper-personalization – increased to 54% from last year
- Lower cost and higher efficiency – 40% of production boost expected
- Make way to new possibilities – 10x times better forecasting experience
- Provides depth to dig insights from data – 60% of marketers guided by data strategy
- Improved utilization of resources – 14% increase current economic output
- Improved business returns – 10-20% as per current estimates
These are a few of the well-known advantages of AI-integration in the market and promises unknown possibilities in the future. The collection of statistics proves that the performance of AI has exceeded expectations. Its functions are more than just back-office support in reducing mundane tasks or query robots for 24×7 customer support. It walks ahead by eagagiṇg customers in human-like conversations, learning human behavior, prompt action over response, intelligent surveillance, and more. With AI growing smarter and flooding market responsibility, it’s our time to step in and make a difference in the AI-Digital age.
Without further adieu, let’s hop on to the 10 uptrend collection we have curated for the emerging automation experts –
Reinforcement Learning (RL)– One of the three basic Machine Learning paradigms of supervised learning and unsupervised learning and a well-known change agent for organizations. E-commerce mavens like Alibaba enjoy a 24 % return on sales after integrating RL on optimizing sponsored search campaigns and have designed a real-time bidding model with RL. This makes it perfect for designing bots to perform business planning, strategizing marketing campaigns, search recommendation and system optimization, promotional advertisements, and other self-learned actions. Therefore RL is prized among the effective application of advanced AI.
Integrated Internet of Things (IoT) – AI takes charge of improving the performance of 31 billion IoT installed devices worldwide. Going ahead, for managing household appliances, AIoT has proven to be a near-perfect CRM and personal assistant. And IoT along with AIoT is no longer just used for mechanical repairments of voice captures. With AI in place, manufacturers are banking on AI-assisted speech recognition systems. Further, Alexa researchers report that they have integrated AI and reduced data imbalance and AI error rate by up to 30%. This has resulted in AIoT being a trending tech-application.
AI for NLP – The age of transcribing has come to an end, this mundane task has been taken over by AI. Today, application builders are producing epic learning models that can detect and accurately predict spoken language and recognize the diverse accents of the speakers. Natural Language Processing (NLP) in turn has the potential to make a logical response to the interpreter. The speech-to-text tools have created new paradigms in the world of Speech API Adaptors. As per reports, the NLP market will outgrow 14 times its size (in 2017) and the market valuation will amount to USD 43 billion by 2025.
Edge Computing – As reported by International Data Corporation (IDC) over 42 billion connected IoT devices will be generating more than 79.4 zettabytes (ZB) of data in 2025. And where does the data go? Device developers are now using Edge Computing to manage Big Data from IoT systems. This application provides cloud data for storage to process in real-time and offers personalization and reduces response time, for enhanced user experience.
Recommender Systems – The main motive behind Recommender Systems is to learn and understand users’ needs. The system continually tracks browsing history, purchase patterns, and even customer ratings received from e-commerce sites, online banking systems, media, and other online utility services to gather data points, analyze and offer customized recommendations. According to McKinsey, 35% of Amazon purchases, and about 75% of Netflix viewers are driven by recommendation systems, and the market is projected to be worth USD 12.03 billion by 2025.
Computer Vision (CV) – One of the most lucrative AI-ML tools is making a buzz in the market with its real-time Object Detection System. With further refinements of AI-ML algorithms, CV has reached 99 percent accuracy with comprehensive visual inputs for user’s ease. From automotive, manufacturing to finance, and healthcare it has multiple advantages across industries. Today the market for computer vision and hardware market is expected to touch USD 48.6 billion and a niche technology to learn for a career in data science.
Behavioral Artificial Intelligence (BAI) – Many complain that AI lacks social intelligence to react and reason with humane problems. To debunk the myth, AI-ML is honing a behavioral approach to design applications. BAI can predict human behavior and make near perfect predictions on consumer trends, judging bidding patterns, making sales predictions, and more. It is now widely used in social media, search engine optimization, websites and services, and other offline experiences. With a market size increase in AI, it is one of the important tools to improve AI-ML predictions.
AI Directed Image Recognition – The first thought that comes to mind is Apple iPhone’s most marketed feature, the incredible Face Recognition System. AI Directed Image Recognition is widely used for analyzing security footage for restricted areas, biometrics, identification, virtual personnel verification, and other critical applications. This is also the technology that enables Facebook to magically tag friends in your photos with ease. With virtual systems on the rise, this is a key tool for backend operations, online registration, onboarding protocols, and more.
Botted Health Surveillance – An ethical and proven AI-system for managing the COVID pandemic that created havoc in 2020. It has been reported that the Blue Dot tool by Toronto has scanned over 100,000 Governmental and media data daily with regular alerts about COVID in Wuhan, China. It has an in-built computer vision to analyze data, heat map functions to spot individuals, captured over drones fitted with high configuration cameras to predict the rate of the virus spread. It includes statistical tools integrated into the AI system to generate entire profiling not missing any inputs. The technology was a bane to quarantine avoiders and will be a necessary tool in curbing the next outbreak.
Quantum AI (QAI) – Fascinated by quantum capability AI technocrats are exploring possibilities to reach ultimate computing ability. Moreover, this super combo will potentially solve problems faster, easier, and better with improved accuracy over predictions. It is hoped to bring new opportunities that businesses are yet to discover. From 2020 to 2030, the market is expected to grow by a whopping 56% CAGR and is the next market trend to look out for.
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