"There's no one thing that defines AI. It's more like a tapestry of modern intelligent technologies knit together in a strategic fashion that can then uplift and create a knowledge base that is automated -- where you can extrapolate findings from there." All of us are very intrigued by the future. We all want to know how will the world look like after 50 years. How will humans work, how will we transport ourselves from one place to another, what all gadgets would we have, and what all things will be left only in our nostalgia?These are some of the questions among a myriad of questions that would arise when we think of the future.

Machine learning and artificial intelligence systems are becoming increasingly important in our day-to-day lives. We have become accustomed to asking our smart-home system to turn the light on, ordering delivery food with the tips of our fingers, and having our fit watch tell us how many steps we have taken that day. With technology advancing faster than ever, it is crucial that we are able to differentiate between the various types of artificial intelligence, as well as how each one builds upon one another. Artificial intelligence as a concept is a science -- just like computer science, neuroscience, or mathematics. What is interesting about artificial intelligence, though, is that it encompasses all three of said sciences and more.

While there has been a good deal of discussion about the use of AI in enterprises and the possibility of building ethical AI strategies, new research indicates that, even 10 years from today, it is unlikely that ethical AI design will be widely adopted. The research, based on a survey of 602 technology innovators, business and policy leaders, researchers and activists conducted by Pew Research Center and Elon University, showed that a majority worried that the evolution of AI by 2030 will continue to be primarily focused on optimizing profits and social control and that stakeholders will struggle to achieve a consensus about ethics. When asked whether AI systems being used by organizations will employ ethical principles focused primarily on the public good by 2030, 68% said they will not. The research added that "ethical" implies adopting AI in a manner that is transparent, responsible and accountable. For others, it means ensuring their use of AI remains consistent with laws, regulations, norms, customer expectations and organizational values.

While there has been a good deal of discussion about the use of AI in enterprises and the possibility of building ethical AI strategies, new research indicates that, even 10 years from today, it is unlikely that ethical AI design will be widely adopted. The research, based on a survey of 602 technology innovators, business and policy leaders, researchers and activists conducted by Pew Research Center and Elon University, showed that a majority worried that the evolution of AI by 2030 will continue to be primarily focused on optimizing profits and social control and that stakeholders will struggle to achieve a consensus about ethics. When asked whether AI systems being used by organizations will employ ethical principles focused primarily on the public good by 2030, 68% said they will not. The research added that "ethical" implies adopting AI in a manner that is transparent, responsible and accountable. For others, it means ensuring their use of AI remains consistent with laws, regulations, norms, customer expectations and organizational values.

A collaborative research team claims their artificially intelligent system should be recognized as the rightful inventor of two innovative designs, in a potentially disruptive development in patent law. Patent law is complicated even at the best of times, but a new project led by researchers from the University of Surrey could make it more convoluted still. Called the Artificial Inventor Project, the initiative is "seeking intellectual property rights for the autonomous output of artificial intelligence." As BBC reports, the researchers are claiming that an artificially intelligent system named DABUS is the rightful inventor of two designs, namely a complex, fractal-like system of interlocking food containers and a rhythmic warning light for attracting extra attention. To that end, the researchers are filing patents on behalf of DABUS with the respective patent bodies in the United States, the United Kingdom, and the European Union.

In this digital era, industries are witnessing the ability of multifaceted artificially intelligent systems performing tasks that mimic intelligent human behavior or even beyond. Artificial Intelligence today, manage large chunks of data and perform redundant tasks, allowing the human workforce to focus on core tasks. This saves cost and time and improves productivity significantly. According to Gartner, the number of industries adopting AI has grown over 270% in the last 4 years. Technology giant, Google pledges $25 million USD in a new AI challenge named'AI For Social Good'.

Businesses are increasingly understanding that their employees will need to work alongside artificial intelligence to deliver the best results. Merging AI and human intelligence (HI) has the potential to provide better products and services, tap into different approaches to tasks, and free people from repetitive tasks as well as uncovering previously undiscovered creative solutions to problems. The more work that is shared with an artificially intelligent system, the more innovative solutions can be discovered and the human ability to build connections with customers and clients can flourish. However, recognising that AI can augment human workforces is only the first step. Businesses have to consider a whole host of infrastructural issues, and prepare their workers for a very different style of working.

In the medium to long term, AI expertise must not reside in only a small number of countries – or solely within narrow segments of the population. Governments worldwide must invest in developing and retaining home-grown talent and expertise in AI if their countries are to be independent of the dominant AI expertise that is now typically concentrated in the US and China. And they should work to ensure that engineering talent is nurtured across a broad base in order to mitigate inherent bias issues. Corporations, foundations and governments should allocate funding to develop and deploy AI systems with humanitarian goals. The humanitarian sector could derive significant benefit from such systems, which might for example decrease response times in emergencies.

For the majority of people, it wasn't long ago that Artificial Intelligence was synonymous with digital personal assistants like Siri and Cortana. AI research was kept largely under the radar, and confined to big technology companies. AI is now a business necessity when it comes to understanding and analysing valuable data. Aside from dealing with data, AI is now used by companies across the spectrum to automate processes, detect fraud, respond to customer queries and carry out menial, but important, administrative tasks. Artificial Intelligence is only improving.

Chinese researchers have developed a method to measure the intelligence quotient (IQ) of AI applications and found that Google's technology scored nearly as well as a human six-year old. The researchers also measured applications developed by Baidu, Microsoft and Apple, all of which fared less well. The study was written up by Liu Feng of Beijing Jiaotong University; Yong Shi, the Director of the Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science; and Ying Liu of the School of Economic Management, UCAS. In the study, they attempt to quantify the capabilities of a number of well-known AI technologies. They present the problem thusly: "Quantitative evaluation of artificial intelligence currently in fact faces two important challenges: there is no unified model of an artificially intelligent system, and there is no unified model for comparing artificially intelligent systems with human beings." In the paper they published, the authors propose to solve that by developing a "standard intelligence model" that attempts to encompass AI systems and humans, and which categories them across a seven-level taxonomy of knowledge capability.