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Artificial Intelligence and Machine Learning have the potential to radically transform the way that organisations utilise the vast amounts of data that they collect. In this video, Rise has interviewed some leading startups to get their thoughts on the future of AI and ML.

In a world of ubiquitous smart-phone cameras, drones, and Google Street View cars, there's probably never been a more important time to start protecting the identities of people unwittingly captured in photos and videos. But while websites like YouTube have started offering tools to obscure faces and other objects appearing in digital media, researchers have found that those protections can be defeated at an alarming rate thanks to recent advances in artificial intelligence. In a paper released earlier this month, researchers at UT Austin and Cornell University demonstrate that faces and objects obscured by blurring, pixelation, and a recently-proposed privacy system called P3 can be successfully identified by a neural network trained on image datasets--in some cases at a more consistent rate than humans. "We argue that humans may no longer be the'gold standard' for extracting information from visual data," the researchers write. "Recent advances in machine learning based on artificial neural networks have led to dramatic improvements in the state of the art for automated image recognition. Trained machine learning models now outperform humans on tasks such as object recognition and determining the geographic location of an image."

The pascal visual object classes (voc) challenge.

The implications of artificial intelligence (AI) for customer service are staggering and truly limitless. But until now, most customer service leaders have been unable to put intelligence in action. They lacked the army of data scientists needed to make sense of the data. And even if they were able to find and hire the data scientists - then what? How to integrate it with their existing customer service technology stack?

BANGALORE: Advancement in robotics, autonomous transport, AI and machine learning could impact more than 5 million people's jobs by 2020, according to World Economic Forum's recent study. 'The Future of Jobs' study states that over 5 million people's jobs are estimated to get impacted by technology advancement across verticals and some 2 million new highly-skilled jobs will be created by 2020. This study is one more indicator of how the wave of automation is expected to sweep across industries. And the global BPO (business process outsourcing) sector is too under its impact with the rise of AI, machine learning and RPA (robotic process automation) technology led automated services. Though, the automation wave is at its early stage, some of the Indian BPO firms led by their CIOs (Chief Information Officers) are already embracing it openly as a way to move forward by adopting new technology and enhancing employees' skills.

Think about the future of automation and AI. The future of automation is here and now. Only, it isn't an edge of the seat sci-fi thriller that you might imagine. Automation is no longer an option in the 21st century enterprise. Across industries, it is already driving efficiency, productivity, agility, adaptability and optimisation.

I have been building technology start-ups since 2003. Throughout the years I observed a trend towards the commoditization of machine learning algorithms and the data wrangling tools to deploy these techniques in the real world. The team at Seldon had been hand-crafting recommendation algorithms for a number of years. We adopted Hadoop back in 2011 in order to scale our data processing capabilities beyond programmatic and relational databases. Hadoop had a sister called project Apache Mahout that bundled a variety of machine learning algorithms.

Machine learning is an enigma to most. For decades it's a been a field dominated by scientists and the few organizations with enough computing power to run complex algorithms against huge datasets. But now the world of machine learning and predictive analytics is opening up to developers and companies of all sizes, with machine learning (ML) providers offering their products through a subscription-based model or open sourcing some of their technology. Data scientists are growing in number, but only in the tens of thousands... There may not be enough to go around.

You could see that it has 13 categories, 910 companies, and an average funding of 10 Million per company. At Venture Scanner, we are currently tracking over 910 Artificial Intelligence companies in 13 categories across 63 countries, with a total of 3.68 Billion in funding. To see the full list of 910 Artificial Intelligence startups, contact us using the form on www.venturescanner.com. Venture Scanner enables corporations to research, identify, and connect with the most innovative technologies and companies. We do this through a unique combination of our data, technology, and expert analysts.