5 Ways CTOS Are Using ML in 2019
Ever since the beginning of time, man has been in search of new ways to make life easier. Machine Learning and artificial intelligence are facets of the latest technological advancements that promise greater convenience in performing daily tasks at increased levels of efficiency, and only highlight how far we’ve come.
Although ML technology isn’t necessarily new, recent developments that enable machines to learn and adapt from past experiences and data sets, have garnered special attention in the scope of the business, health, lifestyle, and entertainment. The ability to make otherwise repetitive sequences of tasks more impactful and affordable, all the while, increasing returns and generating more accurate solutions, unlocks a myriad of opportunities that most of us can’t help but be fascinated by.
That said, many of us don’t realize how prevalent and useful such developments actually are, especially for aspiring entrepreneurs and small business owners. Contrary to popular belief, investing in ML is not as expensive as one might think, and can, in fact, be more rewarding in the long run.
Here are five basic ways that CTOs and owners of small and emerging businesses are using ML in 2019, without necessarily realizing it.
1. Virtual Assistance
Machine learning plays an important role in gathering and optimizing data on the basis of your past experience with such personal aids. This data set is then used to produce results suited to your requirements.
Some of the common examples of virtual assistants are Siri, Alexa, and Google Now. They help find information when requested via the voice, as the name implies. Simply enable them and ask “What’s my schedule today? Your personal helper will look for information for answers, remind you of your requests or send an order (such as phone app) to other resources for the collection of data. For certain tasks, you can even teach assistants such as “Set my alarm for 6:00 a.m. next day”
2. Social Media Services
Social media platforms use machine learning for their own and consumer benefit, ranging from the personalization of your news feed to better target advertising. Here are some examples you have to find, use and enjoy in your social media accounts, without understanding that these marvelous apps are nothing more than ML applications.
Machine learning is based on a simple notion: an interpretation of experiences. The people you interact with, the pages you frequently visit, your interests, and your place of work are all being noticed and recorded by Facebook constantly. Based on continuous learning, you can become friends with a number of Facebook users.
Machine learning, a method used to derive useful information from images and videos, is a core element of Computer vision. Pinterest uses this software to suggest similar pins or images to its users.
3. Malware Detection and Email Spam
Each day more than 325,000 malicious malware is found and the code is 90-98 percent similar to the previous version of each malware. The machine-learning driven security programs understand the pattern of the coding. Consequently, they easily detect and defend against new malware with 2-10 percent variability.
There is a range of email clients ‘ spam filtering strategies. They are powered by machine learning to verify that these spam filters are continuously updated. Once spam filters are disabled, the new tricks adopted by spammers are not monitored. Multi-Layer Perceptron, C 4.5 Decision Tree Induction are among the ML-enabled spam detection strategies.
4. Map Routes and Traffic Predictions
GPS navigation services are used by all of us. Our actual locations and speeds are stored for traffic management on a central server while we do so. This data is then used to create a traffic map. Although this helps to prevent transport and analyzes traffic, the underlying problem is that fewer vehicles are fitted with GPS. In such cases, machine learning helps to quantify places where congestion can be identified based on everyday interpretations and data collection.
When we book a cab through various car booking applications, that particular app estimates the price of the ride. How do you minimize the detours when you share these services? Machine learning is your correct answer. In an interview with Jeff Schneider, the technology lead at Uber ATC, they used ML to identify hours of price increases by predicting ride demand. ML plays an important role throughout the duty process.
5. Refining Search Engine Results
In order to improve search results, Google and other search engines use machine learning. The algorithms in the background keep a look at how you react to the results, every time you conduct a query. The search engine believes the results that are shown are compatible with this query when you open the top results and stay long on the web page. Similarly, the search engine reports that the results you have received did not match the need for the second or third page of search results, but did not open any of the pages. This enhances the search results by the algorithms using ML in the background.
ML AND ENTREPRENEURS
Machine learning enables entrepreneurs to work efficiently, performing a large number of tasks that were traditionally time-consuming, in just a matter of minutes. Integrating ML into organizations can lead to an increase in the operating efficiency of the organization. With these resources more open to companies and consumers, most engineering analysts wonder what the next major breakout would be.
Machine learning basically involves the instruction of an artificial intelligence program. This might resemble the beginning of a cliché science fiction horror story, but for small companies, the results are very encouraging.
The field of machine learning (ML) is one of the most impressive technological developments that has taken our world by storm. Even though many people don’t recognize it yet, ML is already deeply embedded in a number of daily activities, from sharing things on social media to researching about competitor activity and latest industry trends. The scope of opportunities that its application represents to entrepreneurs and leaders seems too good to be true – greater levels of efficiency, faster workaround times, lower costs and accurate results.