Did you know that 53% of companies use data analytics technology? Most of these companies have found that is is very useful. It can be even more valuable when used in conjunction with machine learning. There are a lot of benefits of using analytics to help run a business. You will get even more value out of analytics if you leverage machine learning at the same time.

Machine learning in finance is one of the biggest buzzwords in the finance sector for so long, but no more. Over the past decade, technological progress caught up with real life, with data science revolutionizing the entire industry. The digitalization of investment and money matters can give you all the edge you need. Finance companies that have no way to leverage machine learning are being left behind. By combining machine learning with many elements of big data, companies can take advantage of financial opportunities that almost nobody takes.

Meet Taktile, a new startup that is working on a machine learning platform for financial services companies. This isn't the first company that wants to leverage machine learning for financial products. But Taktile wants to differentiate itself from competitors by making it way easier to get started and switch to AI-powered models. A few years ago, when you could read'machine learning' and'artificial intelligence' in every single pitch deck, some startups chose to focus on the financial industry in particular. It makes sense as banks and insurance companies gather a ton of data and know a lot of information about their customers.

It's no secret that voice recognition has advanced significantly since IBM introduced its first speech recognition machine in 1962. With voice-driven applications like Amazon's Alexa, Apple's Siri, Microsoft's Cortana, and many voice-responsive features of Google, voice recognition has become increasingly embedded in our daily lives as technology has evolved. Every new voice-interactive device we introduce into our lives, from phones to computers to watches to refrigerators, increases our reliance on artificial intelligence (AI) and machine learning. Artificial intelligence is one disruptive technology that has altered the way valuable data is handled. When working with large analyzable sets of data, such as text, machine learning is thought to be at its best.

Think of artificial intelligence (AI), deep learning (DL) and machine learning (ML) as the layers of an onion. Think about machine learning as a part of AI, but AI does not always utilize machine learning methods. Machine learning refers to an algorithm that can create abstractions (models) by training on a dataset and is a method of training an algorithm to accomplish a task. Rapid advances in big data, data analytics, and machine learning are used to convert millions of scattered data points into databases for use in various cybersecurity arenas, such as threat intelligence analysis. Therefore, businesses seeking to leverage machine learning enabled technology need to threat model and perform risk assessments when creating machine learning systems for cybersecurity purposes. Therefore, businesses seeking to leverage machine learning enabled technology need to threat model and perform risk assessments when creating machine learning systems for cybersecurity purposes.

The value of your data is only as good as the actions you're able to glean from it. As the amount of data collected grows in both variety and volume, companies need assistance to help them gain insights into their customers to decipher connections, identify new behavior patterns, predict spending likelihood and assess lifetime customer value. Companies are looking for some edge -- any edge -- as customer spending habits can shift almost overnight. To give them that edge, many marketing departments are exploring machine learning tools for marketing, according to the latest Gartner research.(1) One way to leverage machine learning (ML) is through customer data platforms with ML capabilities built in.

With traditional data management and analysis methods becoming increasingly ineffective given the large volume of information to process, advertisers are looking more and more toward artificial intelligence (AI) and machine learning (ML) technologies for solutions. Being able to constantly learn and adapt, ML in particular can have multiple applications in the ad industry, ranging from data processing and analysis to cognitive intelligence and insights into an organization's target demographic. Properly implemented, ML technologies can provide accurate information about customers' habits, needs and preferences, allowing advertisers to personalize and optimize their campaigns for better results and ultimately higher return on investment. And this is just the tip of the iceberg. As ML technologies continue to evolve, organizations will find new applications to further optimize operations and promote innovation.

Brand Aware

Have you ever used a maps app that helped you determine the fastest route based on current traffic? Or perhaps you've asked something to Siri, Alexa, or Google when you didn't feel like manually looking it up? You may not have realized it at the time, but you were using machine learning technology. Machine learning now plays an integral role in our daily lives. From the product recommendations we see when we shop online, to voice recognition capabilities on our devices, machine learning has had an impact on the way consumers live their lives.

We are tracking the most impactful AI use cases here. This is meant to be a list that grows over time so feel free to contribute with your comments, this list is definitely not comprehensive now. And share the knowledge with your twitter followers: @AndrewYNg claims that "AI is the new electricity". We compiled 100 applications runnning on this new electricity. Marketing can be summarized as reaching the customer with the right offer, the right message, at the right time, through the right channel while constantly learning. Optimizing product, pricing & placement allows marketers to create an attractive value proposition to customers. Gesture Control: Gesture control enables higher levels of activity and engagement by allowing users another mode of interaction with your digital products. Quantify the gesture levels and other engagements in order to provide meaningful insights. Pricing Optimization: Also called dynamic pricing or demand pricing, pricing optimization allows companies to optimize markdowns. Optimal markdowns minimize cannibalization while maximizing revenues. One of the easiest transformations a business can achieve, dynamic prices directly impact the bottom line and can be rolled out in a matter of days. Optimize markdowns to minimize cannibalization while maximizing revenues. Identify which products are of significant importance for customers.