Predictive analytics involves applying a wide range of machine learning algorithms to gain predictive power. It is more or less synonymous with machine learning, the most popular and widespread variant of AI, comprised of a set of algorithms that learns to model data by observing large chunks of it. Machine learning is used by startups to big corporations today to make use of large amounts of data they gather and generate everyday. Predictive ability is obtained through sophisticated modeling of data and key to success is applying right algorithm for right problem. This decision depends on multiple factors such as data volume, type, velocity and also on the specific problem you are trying to solve. Here, we help you not only by guiding you to pick right tools but also by building solutions with powerful predictive capabilities.
Deep learning is a subset of machine learning that utilizes neural networks with many hidden layers. It has a history of over 50 years but recent high availability of data and computational resources made real-world applications feasible and successful. Deep learning is the underlying technology behind most of the notable applications from Apple Siri (intelligent assistant) to Tesla Autopilot (autonomous driving). With large amounts of right data, deep learning can bring high predictive power and understanding of complex and unstructured data types such as natural language, images and video. It may require significant computational resources specially on large datasets, thus considering deep learning as a silver bullet can be an overkill unless you make the right choice.
Modern day computer vision is a powerful crossover of traditional computer vision and deep learning. It focuses on analysing image and video content that have become widespread with the emergence of social media and consumer devices that can produce rich multimedia content. Computer vision has become immensely helpful in analysing these rich data types. It can be used to perform many tasks ranging from simple object detection to more sophisticated applications such as virtual fitting room. Yet most of the multimedia content that can be used to generate valuable insights is thrown away unanalysed today, one of the best examples being CCTV video footage. We add value to your business by identifying and analysing right multimedia content using our powerful computer vision stack.
Human-like natural language understanding is sometimes considered as the holy grail of AI. Human languages are inherently complex for machines. But if we are to analyse and understand large amounts of data generated by humans everyday, we need AI technology to decipher for machines. The emergence of advanced NLP techniques and deep learning have made this possible, best examples being chatbots and intelligent assistants. NLP gives you the ability to interact better with your customers and understand them better, which eventually brings competitive advantage to domains ranging from marketing to healthcare. We offer a wide range of NLP capabilities to transform your data into insights no matter which domain you are from.
Keen to explore the latest developments in AI and to learn more about how it was being used in the region, I attended the inaugural AI Everything summit in Dubai. Having also recently been thinking about the current state of AI in Sri Lanka and what it could be (or should be) in the future, I found that the summit provided some very useful perspectives. The perspectives were in the context of two themes.
This is going to be a short one. While others argue over GPT-2, we had to dig a little deeper into Google BERT. If you are a follower of deep learning research, specially on NLP side, BERT needs no introduction at this point in time. Since its initial release last October, dozens more resources have been added highlighting the significance of this work by Google.
According to Gartner analysts, up to 85% of big data projects fail. I suspect that for AI projects, especially if you count the ones that never start, the failure rate is even higher. What are the reasons for the high failure rate with AI projects?
Convolution is an extremely effective technique that can capture useful features from data distributions. Specifically, convolution based deep neural networks have performed exceedingly well on 2D representation learning tasks, e.g. image analysis. Given this success, it is natural to investigate how to use this concept to capture features in different settings.
A couple of weeks ago I attended the World Summit AI 2018 in Amsterdam. Coming from a non-technology background (I originally trained as a medical doctor) and as someone who had since worked as a management consultant and more recently have been working with various startups (including ConscientAI), I was keen to understand more about AI from a business point of view.
A 2017 survey of more than 3,000 executives by MIT Sloan Management Review and Boston Consulting Group revealed that almost 85% believed that Artificial Intelligence (AI) will give their companies or help them maintain a competitive advantage . Non-public AI companies raised $15.2B funding in 2017, an increase of 141% compared to 2016 .
Yes, that's correct. Well, I think so and let me explain. I think the biggest challenge of AI right now is making it an industry or an engineering field. All the other challenges were there for a while if you really think about it.
Since Ian Goodfellow first proposed the idea of GANs ( https://arxiv.org/abs/1406.2661), it has become a buzz word within ML community, simply because it works stunningly well (given that you came up with a perfect architecture). Many people, specially Yann LeCun, a who is considered as one of the giants in Deep Learning, stated at some point that GANs are a significant breakthrough in deep learning.
We are an artificial intelligence (AI) technology company that focuses on applying machine learning and deep learning to solve problems in multiple domains. We make AI accessible to businesses. We are on a quest to transform traditional and emerging industries across the world with AI.
Our team consists of highly experienced talent from world-leading institutions and enterprises. In our prior work we have applied AI technologies in enterprise software, telecommunication, remote sensing and social media. We have presented our work at top conferences and published in international journals. We work with our digital product engineering partner Calcey Technologies to deploy enterprise solutions that are robust, secure and scalable.
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