Artificial intelligence, still in its infancy, has the potential to change business forever. Today, the emerging technology is used mostly by large enterprises through machine learning and predictive analytics. Ahead of the digital marketing conference, we thought it a good time to understand the current state of AI and what lies ahead. Machine learning is the second-most widely used technology among enterprise organizations, at 24%, followed by virtual personal assistants, at 15%.
In the last episode How to master optimisation in deep learning I explained some of the most challenging tasks of deep learning and some methodologies and algorithms to improve the speed of convergence of a minimisation method for deep learning. I explored the family of gradient descent methods - even though not exhaustively - giving a list of approaches that deep learning researchers are considering for different scenarios. Every method has its own benefits and drawbacks, pretty much depending on the type of data, and data sparsity. In this episode I would like to continue that conversation about some additional strategies for optimising gradient descent in deep learning and introduce you to some tricks that might come useful when your neural network stops learning from data or when the learning process becomes so slow that it really seems it reached a plateau even by feeding in fresh data.
In this sense, the test is still useful but trading strategy developers know that good performance in out-of-samples for strategies developed via multiple comparisons is in most cases a random result. 1 with two examples that correspond to two major market regimes, highly significant strategies even after corrections for bias are applied can also fail due to changing markets. Conclusion: Robustness tests and stochastic modeling in general can assess over-fitting conditions but Type-I error (false discoveries) is high especially in the case of multiple comparisons even when applied to an out-of-sample. Most validation tests done by practitioners but also academics suffer from either multiple comparisons bias or fail under changing market conditions.
Statistical solutions use inductive rationale to predict outcome based on voluminous historical data. Disruptive changes require deductive and abductive rationale to find solutions on new facts when historical data on such facts does not exists. We propose a comprehensive solution that would enable enterprises to use Artificial General Intelligence (AGI) to discover new relevant subjects, so as to discover and augment existing quantitative analysis with new or previously unknown domain variables. The real-time aspect of tactical and strategic execution requires new analytic methods on qualitative data.Meta Vision Analysis and Bionic Fusion Analysis enable enterprise organizations to transcend this limitation and dynamically discover relevant domain variables, drawing insights for real-time execution by "connecting the dots".
AI-powered solutions can help entrepreneurs automate their business communication, generate insights from phone calls, sales and marketing data, create intelligent social media and content strategies, and much more. It is an online application powered by machine learning algorithms that recognize stylistic and semantic errors, wrong sentence structure and other nuanced language features that only a professional editor would help you fix. Powered by NLP and deep learning, Legal Robot creates high-level legal models from large corpora of contracts that cover a broad range of business cases and scenarios. As an entrepreneur, you may opt for a Business plan ($99) that includes unlimited calling, unlimited AI-based notes and 50 cents/minute for the full transcription of your calls.
Join this VB Live event to learn how predictive analytics and AI can deliver valuable insights and power more personalized user experiences. AI and machine learning have become the building blocks of bank profitability and growth (think customer engagement, loyalty, and the financial wellness tools). Machine learning powers predictive analytics: the ability to find patterns and then apply those patterns to other data. Learn how to drive customer engagement and loyalty with the next wave of personalized financial wellness solutions when you join this interactive VB Live event.
Freed from human-dictated logic, modern AI systems use multi-layered neural networks to store and categorize information in their own ways, and find their own "organic" ways of generalizing from examples, finding relationships, categorizing data and finding patterns. Poor data quality or training can result in biased outcomes -- essentially, a poorly educated computer that will not be a good problem solver going forward. Address the black box: The black box nature of AI systems is not simply an interesting feature; rather, it creates a set of novel issues in terms of risk allocation. In addition, modern AI systems may create insights that present acute sensitivity concerns, and AI functionalities may create new relationships among data owners.
There are increasing calls for government oversight of artificial intelligence development. AI is undoubtedly set to offer solutions that will do much to change our lives for the better and grow economies, but how important is it that we have a so called "human in command" approach to AI? These include ethics, safety, transparency, labor, privacy and standards, education, access, laws and regulations, governance, democracy, warfare and "superintelligence". From whichever angle you look, the general consensus seems to be that AI will have an enormous impact on our lives and it is a technology governments, industries and society need to be fully prepared for.
Capabilities like voice recognition, natural-language processing (NLP), image processing and others benefit from advances in big data processing and advanced analytical methods such as machine learning and deep learning. These skills include technical knowledge in specific AI technologies, data science, maintaining quality data, problem domain expertise, and skills to monitor, maintain and govern the environment. Capabilities like voice recognition, natural-language processing (NLP), image processing and others benefit from advances in big data processing and advanced analytical methods such as machine learning and deep learning. These skills include technical knowledge in specific AI technologies, data science, maintaining quality data, problem domain expertise, and skills to monitor, maintain and govern the environment.
"Gradient masking" is a term introduced in Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples. If the model's output is "99.9% airplane, 0.1% cat", then a little tiny change to the input gives a little tiny change to the output, and the gradient tells us which changes will increase the probability of the "cat" class. The defense strategies that perform gradient masking typically result in a model that is very smooth in specific directions and neighborhoods of training points, which makes it harder for the adversary to find gradients indicating good candidate directions to perturb the input in a damaging way for the model. Neither algorithm was explicitly designed to perform gradient masking, but gradient masking is apparently a defense that machine learning algorithms can invent relatively easily when they are trained to defend themselves and not given specific instructions about how to do so.