If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
During the 2008 financial crisis, the banking industry realized that their machine learning algorithms were based on flawed assumptions. So financial system regulators decided that additional controls were needed, and regulatory requirements for "model risk" management on banks and insurers were introduced. Banks also had to prove that they understood the models they were using, so, regrettably but understandably, they deliberately limited the complexity of their technology, resorting to generalized linear models that offered simplicity and interpretability above all else. In the past several years, machine learning and AI have made enormous strides in accuracy. Yet regulated industries (like banking) remain hesitant, often prioritizing regulatory compliance and algorithm interpretability over accuracy and efficiency.
We are living in a world of data overload. From behavioral analytics to customer preferences, businesses now have so much data at their fingertips that they're unable to process and consume all of it in a meaningful way. This is where the magic of machine learning comes in. When applied to massive internal company datasets, machine learning technology can derive important insights and provide actionable recommendations and predictions at superhuman scale. But as automation, machine learning, and artificial intelligence technologies continue to show up in our daily experiences, more and more users are asking questions.
California regulators are embracing a General Motors recommendation that would help makers of self-driving cars avoid paying for accidents and other trouble, raising concerns that the proposal will put an unfair burden on vehicle owners. If adopted, the regulations drafted by the California Department of Motor Vehicles would protect these carmakers from lawsuits in cases where vehicles haven't been maintained according to manufacturer specifications. That could open a loophole for automakers to skirt responsibility for accidents, injuries and deaths caused by defective autonomous vehicles, said Armand Feliciano, vice president for the Association of California Insurance Companies. The regulations drafted by the California DMV would protect carmakers from lawsuits in cases where their self driving vehicles haven't been maintained according to manufacturer specifications. The regulations drafted by the California Department of Motor Vehicles would protect these carmakers from lawsuits in cases where vehicles haven't been maintained according to manufacturer specifications.
Artificial Intelligence (shortened to AI) falls under the computer science umbrella. It refers to the discipline of programming computers to become intelligent and make decisions, just as a human would do. Its primary purpose is to replace/complement humans when making sophisticated decisions, using data inputted into a system and then injecting code to help the computers make more intelligent decisions based on possible outcomes. Although AI is said to be nothing but a gimmick by some critics, it has the potential to be highly useful in the world of business, helping organisations become more automated, freeing up a human's role to make the decisions only a human brain can make. Using AI significantly speeds up the time it takes for a process to happen and with so much data being generated every single second, automating this process can make life easier for everyone.
Those worried that the rise of artificial intelligence means that robots will take their job might feel comforted by the fact that many AI tools are actually being designed not to replace humans, but to help them do their jobs better. Though the field is still in its infancy, many young startups came to Europe's largest tech conference, Web Summit, last week to showcase how their AI tools are working to make people more efficient and productive, in both their personal and professional lives. Here are a few that stood out. Does it feel like you've never got enough time for the things you really want to do in life? Paris-based AI startup Smarter Time is helping its 80,000 users find the time that always seems to be missing by tracking their habits and providing feedback.
Personalized experiences are a hot topic these days. Certain types of businesses have become very skilled at delivering personalized service. Think about a hotel you've stayed at before that welcomes you back and remembers that you liked a certain type of pillow, a specific newspaper and a corner room. The experience is becoming more and more common, and this type of service is crossing over into many other industries, especially retail. When a customer walks into a retail store, the salesperson has two choices: simply ring up a purchase, or truly help the customer get what he or she really needs.
The release of two machine learning (ML) model builders have made it easier for software engineers to create and run ML models, even without specialized training. Microsoft and Amazon Web Services' (AWS) Gluon is an open source project that eliminates some of the difficult work required to develop artificial intelligence (AI) systems. It provides training algorithms and neural network models, two important components of a deep learning system, that developers can use to develop their own ML systems. Google's ML engine is part of its cloud platform and is offered as a managed service for developers to build ML models that work on any type of data, of any size. Similar to Gluon, Google's service provides pre-trained models for developers to generate their own tailored ML models.
A year ago, the AI Now Institute released its inaugural report on the near-future social and economic consequences of AI, drawing on input from a diverse expert panel representing a spectrum of disciplines; now they've released a followup, with ten clear recommendations for AI implementations in the public and private sector. The first of these is "Core public agencies, such as those responsible for criminal justice, healthcare, welfare, and education (e.g "high stakes" domains) should no longer use'black box' AI and algorithmic systems." The remaining recommendations deal with operational details, like examining training data for bias and validating the performance of the models to ensure that they aren't misfiring; and areas where work needs to be done, like evaluation of the impact of AI on hiring and HR, setting data-set quality standards; bringing cross-disciplinary expertise to bias evaluation; and the active inclusion of women, minorities and other marginalized populations in systems design and evaluation. This includes the unreviewed or unvalidated use of pre-trained models, AI systems licensed from third party vendors, and algorithmic processes created in-house. The use of such systems by public agencies raises serious due process concerns, and at a minimum such systems should be available for public auditing, testing, and review, and subject to accountability standards.
This could help you in building your first project! Be it a fresher or an experienced professional in data science, doing voluntary projects always adds to one's candidature. My sole reason behind writing this article is to get your started with recommendation systems so that you can build one. If you struggle to get open data, write to me in comments. Recommendation engines are nothing but an automated form of a "shop counter guy".
To Salesforce customer Bill Hoffman, chief analytics officer at Minneapolis-based US Bank, the "A" in "AI" is about "augmented" intelligence because, as he said in a keynote at this week's Dreamforce event in San Francisco, "there's nothing artificial about it." US Bank has deployed Salesforce Einstein capabilities including Predictive Lead Scoring and Einstein Analytics (formerly known as Wave) for customer attrition analysis and retention efforts. It's also using Einstein Discovery (formerly BeyondCore) to better understand customer behavior and cross-sell opportunities. The bank expects to roll out Einstein capabilities to more than 2,000 of its customer-facing financial advisers across the firm in hopes of "personalizing service at scale" and "creating a differentiated customer experience," Hoffman said. Personalizing at scale is precisely the idea behind two "myEinstein" capabilities announced at Dreamforce.