Securing patents for inventions that use artificial intelligence (AI) and machine learning can be challenging for innovators of these ground-breaking technologies, which attempt to use the processing power of computers to replicate the intelligence and learning capabilities of humans. Without patent or other intellectual property protection, they may be unable to commercialise their inventions, which could undermine investment in this dynamic field of research and development. To clear the way for innovators, the European Patent Office has recently amended its'Guidelines for Examination' by including a new section containing advice about how patents related to AI and machine learning technologies should be assessed. The guidance clarifies that whilst algorithms are regarded as'computational' and abstract in nature, which means they are not patentable per se, once applied to a technical problem they may become eligible for patent protection. Beneficially, the approach outlined in the guidance is similar to that currently used to assess the patentability of computer-implemented inventions.
You sit down to watch a movie and ask Netflix for help. Zoolander 2?") The Netflix recommendation algorithm predicts what movie you'd like by mining data on millions of previous movie-watchers using sophisticated machine learning tools. And then the next day you go to work and every one of your agencies will make hiring decisions with little idea of which candidates would be good workers; community college students will be largely left to their own devices to decide which courses are too hard or too easy for them; and your social service system will implement a reactive rather than preventive approach to homelessness because they don't believe it's possible to forecast which families will wind up on the streets. You'd love to move your city's use of predictive analytics into the 21st century, or at least into the 20th century. You just hired a pair of 24-year-old computer programmers to run your data science team. But should they be the ones to decide which problems are amenable to these tools? Or to decide what success looks like?
The next big thing in the social sector has officially arrived. Machine learning is now at the center of international conferences, $25 million dollar funding competitions, fellowships at prestigious universities, and Davos-launched initiatives. Yet amidst all of the hype, it can be difficult to understand which social sector problems machine learning is best positioned to solve, how organizations can practically use it to enhance their impact, and what kind of sector-wide investments can enable the ambitious use of it for social good in the future. Our work at IDinsight, a nonprofit that uses data and evidence to help leaders in the social sector combat poverty, and the work of other organizations offer some insights into these questions. Machine learning uses data (usually a lot) and statistical algorithms to predict something unknown.
I usually see artificial intelligence explained in one of two ways: through the increasingly sensationalist perspective of the media or through dense scientific literature riddled with superfluous language and field-specific terms. There's a less publicized area between these extremes where I think literature needs to step up a bit. News about "breakthroughs" like that stupid robot Sophia hype up A.I. to be something akin to human consciousness while in reality, Sophia is about as sophisticated as AOL Instant Messenger's SmarterChild. Scientific literature can be even worse, causing even the most driven researcher's eyes to glaze over after a few paragraphs of gratuitous pseudo-intellectual trash. In order to accurately assess A.I., the general population needs to know what it really is.
Andrew NG is a computer scientist, executive, investor, entrepreneur, and one of the leading experts in Artificial Intelligence. He is the former Vice President and Chief Scientist of Baidu, an adjunct professor at Stanford University, the creator of one of the most popular online courses for machine learning, the co-founder of Coursera.com At Baidu, he was significantly involved in expanding their AI team into several thousand people. The book starts with a little story. Imagine, you want to build the leading cat detector system as a company.
The future of the language industry is bright. In a world where globalization brings us closer together, advances in technology make it easier than ever to communicate and conduct our work efficiently. The primary purpose of a machine is to facilitate a specific task; so, the question remains, why do so many of us fear the rise of artificial intelligence (AI)? Admittedly, the notion of a machine learning to navigate an area so intimately human as language is disquieting. Where do humans fit in an industry that is so eager to introduce machine learning technologies?
Traditional approaches to leadership development no longer meet the needs of organizations or individuals. There are three: (1) Organizations, which pay for leadership development, don't always benefit as much as individual learners do. A growing assortment of online courses, social platforms, and learning tools from both traditional providers and upstarts is helping to close the gaps. The need for leadership development has never been more urgent. Companies of all sorts realize that to survive in today's volatile, uncertain, complex, and ambiguous environment, they need leadership skills and organizational capabilities different from those that helped them succeed in the past. There is also a growing recognition that leadership development should not be restricted to the few who are in or close to the C-suite. With the proliferation of collaborative problem-solving platforms and digital "adhocracies" that emphasize individual initiative, employees across the board are increasingly expected to make consequential decisions that align with corporate strategy and culture.
If you're wondering which of the growing suite of programming language libraries and tools are a good choice for implementing machine-learning models then help is at hand. More than 1,300 people mainly working in the tech, finance and healthcare revealed which machine-learning technologies they use at their firms, in a new O'Reilly survey. The list is a mix of software frameworks and libraries for data science favorite Python, big data platforms, and cloud-based services that handle each stage of the machine-learning pipeline. Most firms are still at the evaluation stage when it comes to using machine learning, or AI as the report refers to it, and the most common tools being implemented were those for'model visualization' and'automated model search and hyperparameter tuning'. Unsurprisingly, the most common form of ML being used was supervised learning, where a machine-learning model is trained using large amounts of labelled data.
Deep learning (DL) has transformed much of AI, and demonstrated how machine learning can make a difference in the real world. Its core technology is gradient descent, which has been used in neural networks since the 1980s. However, massive expansion of available training data and compute gave it a new instantiation that significantly increased its power. Evolutionary computation (EC) is on the verge of a similar breakthrough. Importantly, however, EC addresses a different but equally far-reaching problem.