- The 28-page report, titled AI-augmented Government, examines several case studies, provides a taxonomy of AI systems, and concludes that in the federal government alone, automation with “high investment” could free up as many as 1.2 billion hours of work and save up to $41.1 billion annually.
- Through the use of rules-based systems, machine translation, computer vision, machine learning, robotics and natural language processing, the report notes the unusual but “tantalizing” paradigm presented by AI in which speed is increased, quality is improved, and cost is reduced — all in parallel.
- William Eggers, a co-author of the report and executive director of the Deloitte Center for Government Insights, told StateScoop that automation technologies are now improving in performance at an exponential rate.
- Report authors note that automation can be broken into four types:
The lesson to learn from these different types of automation, Eggers said, is that adoption of automation is a two-stage process: First government should automate every task it can, and then it should search for ways to augment human tasks.
- Government agencies looking to use automation for the first time will find an easy entrée in robotic process automation, Eggers said.
Technologies like computer vision, machine learning and natural language processing will transform government at all levels sooner than people think, researchers report.
Continue reading “AI could save government $41 billion, report says”
- One might not normally equate AI development — which can involve complicated, cutting-edge technologies such as cognitive computing, machine learning, deep learning and so on in long-term initiatives — as something suitable for freelance projects, but news from freelancing firm Upwork indicates that’s the case.
- Upwork, which matches employers with freelancers, published data from its Web site that showed AI was the second-fastest-growing in-demand skill in the first quarter of the year.
- But, among development-related skills, AI is No. 1, followed by skills such as natural language processing, C++ development, Swift development and others, with the AI-related machine learning skill also making the top 20 list.
- “With artificial intelligence (AI) at the forefront of the conversation around what the future of work holds, it’s no surprise it is the fastest-growing tech skill and the second fastest-growing skill overall,” Upwork said in a statement last week.
- Perhaps surprisingly, that pervasiveness didn’t show up in Upwork’s previous listing of in-demand skills for the third quarter of 2016, in which AI didn’t even place in the top 20 (though machine learning was No. 1):
Artificial intelligence is such a hot topic in software development that companies are even seeking freelancers to help out, making it the No 1. sought-after tech skill in Upwork’s latest quarterly report.
Continue reading “AI Is So Hot, Even Freelancers Are in Demand — ADTmag”
- Caffe is a popular deep learning network for vision recognition.
- Caffe 2 continues the strong support for vision type problems but adds in recurrent neural networks (RNN) and long short term memory (LSTM) networks for natural language processing, handwriting recognition, and time series forecasting.
- MXNet supports deep learning architectures such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) including Long Short-Term Memory (LTSM) networks.
- However, with Facebook’s most recent announcement, it is changing course and making Caffe 2 its primary deep learning framework so it can deploy deep learning on mobile devices.
- DL4J has a rich set of deep network architecture support: RBM, DBN, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), RNTN, and Long Short-Term Memory (LTSM) network.
Open source deep learning neural networks are coming of age. There are several frameworks that are providing advanced machine learning and artificial intelligence (A.I.) capabilities over proprietary solutions. How do you determine which open source framework is best for you?
Continue reading “Which deep learning network is best for you?”
- After this course, I cannot ignore the new developments in deep learning—I will devote one third of my machine learning course to the subject.
- I’m a CEO, not a coder, so the idea that I’d be able to create a GPU deep learning server in the cloud meant learning a lot of new things—but with all the help on the wiki and from the instructors and community on the forum I did it!
- Sometimes I feared whether I would be able to solve any deep learning problems, as all the research papers I read were very mathy beyond reach of simple intuitive terms.
- But Jeremy and Rachel (Course Professors) believe in the theory of ‘Simple is Powerful’, by virtue of which anyone who takes this course will be able to confidently understand the simple techniques behind the ‘magic’ Deep Learning.
- The course exceeded my expectations and showed me first hand how both Deep Learning and ourselves could change the world for better.
fast.ai’s practical deep learning MOOC for coders. Learn CNNs, RNNs, computer vision, NLP, recommendation systems, keras, theano, and much more! neural networks!
Continue reading “Practical Deep Learning For Coders—18 hours of lessons for free”
- People figured that if they could find a way to codify instructions to a machine to tell it what steps to take, any manual operation could be eliminated saving any business time and money.
- Algorithms, on the other hand, are a series of steps that describe a way of solving a problem that meets the criteria of both being correct and ability to be terminated if need be.
- Instead of writing code to search our data given a set of parameters of the certain pattern as traditional coding focuses on, with big data we look for the pattern that matches the data.
- Now another step’s been added to the equation that finds patterns humans don’t see, such as the certain wavelength of light, or data over a certain volume.
- So, this new algorithmic step now successfully searches for patterns and will also create the code needed to do it.
We are all now in what’s called the “big data era,” and we’ve been here for quite some time. Once upon a time we were only just starting to piece together
Continue reading “Why Future Emphasis Should be on Algorithms”
- The third one was in 1969 when the word information technology was introduced in the world, and the fourth one is the revolution of Artificial Intelligence which we are experiencing right now.
- Artificial Intelligence is the set of tools and programs that make any software smart enough that the observer would feel that he is dealing with a human, not with the software.
- Artificial Narrow Intelligence: The first stage of AI as the name suggests is functionally very narrow.
- Artificial General Intelligence: AGI may only be one step further from ANI but this step is the biggest achievement of humanity.
- Artificial Super Intelligence: ASI is the final stage of AI predicted by the scientist in which the machines with ASI can able to pass the average human intelligence.
This world has seen four major revolutions that changed its entire face. The first revolution was in 1784 when the first steam engine introduced in the world. The second one was in 1870when the electricity was invented. The third one was in 1969 when the word information technology was introduced in the world, and the fourth one is the revolution of Artificial Intelligence which we are experiencing right now. The present revolutionary era is based on the extreme automation and global connectivity for which Artificial Intelligence is imperative. Just like the other three revolutions, the changes and developments that this revolution is creating will be the bedrock of our future and our way of interacting with technology and with each other.
Continue reading “The Evolution of Artificial Intelligence”
- Our market report, “The cognitive advantage: Insights from early adopters on driving business value,” reveals that early adopters employ cognitive computing for competitive differentiation.
- In fact, 65 percent say that cognitive adoption is very important to their strategy and success, and more than half regard cognitive computing as a must-have to remain competitive.
- Early adopters leverage a range of capabilities from machine learning to natural language processing to unlock value from a range of data sources, both structured and unstructured.
- All told, early adopters are pushing the boundaries of traditional business practices and unlocking groundbreaking services and sources of value.
- Here are just a few of the ways early adopters are seizing the cognitive advantage:
By becoming a cognitive business, early adopters have been able to evolve customer acquisition, increase customer engagement, and improve customer service.
Continue reading “IBM Cognitive”