Trending Tech Trends
Trending Tech Trends Examples
Trending tech trends is the continuous process of determining when technologies are evolving and changing. This helps analyze how it is affected by changes in society, economy, politics, etc. The key factor to determine which trend will be most successful in a company’s business, however, is how it is used to improve technology products or services.
For this to work well, the topic must always include trends. Trends can often create more confusion than clarity. In fact, there is no single way for trends to occur at any given time. For example, new tech releases all happen in one period, but different types of trends appear. Even companies that have different goals for their product life cycle have similar ideas on what the next step should be. It all comes down to how we view technology going forward. A few examples of the most common tech trends were listed below.
Apple Watch- Apple Incorporation (Apple Inc.) launched its Watch Series 6 in October 2012. Over the years, the watch became one of the best selling consumer electronics devices. With its sleek design and powerful features, users love the new features of the series. However, from a health perspective the series is also notorious for causing wear and tear. As reported by the Washington Post, Apple’s fitness app Appletica has created millions of calorie records across multiple countries. Since 2014, Appletica has been able to identify these records in order to warn users, who may not even know they are having such records set up. While Appletica uses data from the user’s physical activity to help consumers, it does so without the user’s consent. Therefore, Appletica collects personal information and violates consumer privacy laws. Due to the negative publicity surrounding Appletica, Apple recently banned it from iOS 11 apps.
Google Glass- Google Glass was first introduced in 2015, and since then has become increasingly affordable and accessible. Unlike other hardware, glass is much less expensive than traditional devices like smartphones, and it provides high accuracy and durability. The original version of Google Glass released in 2016 and sold over 200K units in September 2017. Today, it has grown into a very important device, with hundreds of thousands of users worldwide. Many people use it to stream movies, play games, and connect on social media. Additionally, people have begun using Glass to read books, as the digital interface allows them to tap on text and scan documents. These advancements make the use of Glass much more intuitive and easier to use. However, Google still has to battle against another threat from Amazon and Apple–the Echo device. From an ad standpoint, the Echo is far more costly than the newer model. Currently, the price is $150 for a 2.2GHz Wi-Fi connection, while the pricier 2.5 GHz version costs around $400. Also, despite the higher end cost, Google Glass only works with Android phones, so it is limited to those platforms. Furthermore, since Glass was initially designed for students, it is expensive and therefore less feasible. If anything happens to the iPhone X, this could impact the popularity of Google Glass in particular.
In the upcoming year this trend will change. Companies specializing in data storage products will rise along with software in the form of Artificial Intelligence (AI). Some examples of AI trends in the coming year will include Natural Language Processing (NLP), Deep Learning, and Speech Recognition. NLP includes machine learning techniques that allow computers to learn and learn from data, including both structured data and unstructured data. Although NLP tends to be associated with computer science, it is actually a lot more than that. Different kinds of problems you could solve would be a natural language processing system, image recognition, speech recognition, etc. All of these can be solved using algorithms that incorporate deep learning and neural networks. Neural network models are commonly applied to these tasks, as well. As an example, a neural network consists of artificial neurons that are connected together as points. They mimic biological neural cells and are modeled after the brain. To perform basic tasks, the computers must learn and understand the structure of the brain itself. In addition, Deep Learning involves simulating the function of our brains in computers and utilizing existing data to develop their own programs. Finally, Speech Recognition involves training the computer to recognize human languages based on past samples of speech.
To sum it up, the main factors driving the most recent technological trends are the constant evolution of industry standards and competition within industries, along with the introduction of new technical advances. In the future, however, new trends will continue to arise, and the current ones will evolve dramatically.