“There were 5 exabytes of information created between the dawn of civilization and 2003, but that much information is now created every 2 days.”Eric Schmidt
The global Artificial Intelligence (AI) spending is projected to exceed $40 billion by 2025 from about $650 million in 2016, driven by the huge potential offered by the capability of algorithms in machine learning to use predictive analysis to simulate human decision making from past data and give improved results. With VC funding estimated at $3.0 billion, US AI companies received the highest VC funding in 2017.
Big Data, deep learning algorithm, and GPU accelerated computing represent the primary building blocks of AI that will drive the transition from the current infancy stage to mass-adoption in various forms such as reasoning, problem solving, predictive analysis, and natural language understanding. Developments in genetic algorithms and neural networks that mimic the human learning process are becoming increasingly relevant to enterprise applications. Machine learning is touted to transform forecasting and real-time predictions in detecting anomalies, recommending products or predicting churn. Myriad applications of machine learning in spotting patterns in cyber security, enable produce sorting in agriculture, generate 3D models in the construction industry, and as a predictive tool in social media are expected to drive widespread adoption of the technology in the coming years. Other major industries focusing on machine learning applications include Internet of Things (IoT), retail, professional/scientific/technical services, media/publishing, manufacturing, financial services, telecommunications, transportation, and utilities/energy.
A growing number of predictive analytical models for IoT are being developed around machine learning. Given that the era of IoT & big data has unleashed a torrent of unstructured data that is chaotic, complex, and dynamic, traditional predictive analytics infrastructure based on linear relationships among cause and effect variables are becoming increasingly unsuitable. The key value of machine learning in the IoT space is its ability to counter the “butterfly effect” defined as tiny unexpected variations in data behaviour in the deluge of big data that has the ability to derail and make predictive models unstable. With big data now beginning to account for a major share of enterprise data, enterprise data is becoming increasingly vulnerable to the butterfly effect. The continued expansion in new IoT endpoints will bring with it infinite data in new combinations that make prediction a more complex activity.
Machine learning has the potential to enable prediction under chaotic conditions and control over the butterfly effect. Machine learning, in this regard, is growing in prominence for its ability to better predict complex data behaviors. Machine learning algorithms helps clean up data by automating the process of discovering patterns and trends in large and diverse datasets, through intelligent programs modify their algorithms based on experience. The technology is of special significance in “Industrial IoT” where decoding the vibe at the machine level can save manufacturing companies billions of dollars lost in idle time by predicting machine repair and breakdown. In the enterprise sector, this kind of data analysis can help predict potential supply chain disruption and a host of other insidious events that fly well below the radar of conventional predictive analytics. Machine learning takes predictive capabilities to the next level of pre-emptive prediction.
Machine learning technologies will stand at the forefront of operational data management capabilities of the future that requires prediction, prevention and pre-emption in real-time. Leading AI companies worldwide include AIBrain, Appier, Apple, Amazon, Deepmind, OpenAI, Baidu, Google, Microsoft Research, Facebook, Samsung, LG, Naver, Banjo, CloudMinds, H2O, IBM, iCarbonX, Intel, Iris AI, Next IT, Salesforce, Twitter, ViSenze, X.ai, and Zebra Medical Vision, among others. Notable AI startups include SoundHound, Endgame, Invoca, C3 IoT, InsideSales.com, CrowdFlower, Narrative Science, ZestFinance,