You've probably already heard about how artificial intelligence (AI) is slowly but surely sweeping the business sector and making things easier for large companies. AI is constantly used to make cost-effective improvements, as well as to gain an advantage over the rest of the competition. That said, what about newer companies? Is AI something that only experienced industry leaders can utilize? It's a valid question to ask, as there are so many struggling startup owners out there looking for any advantage they can use in today's digital world. So how exactly does AI impact startups, and can you use it to your advantage to help expand your new business? A matter of informationThere's no denying that the amount of data available to a new company is staggering. There are many ways to track relevant metrics that it can be overwhelming to most first-time business owners. As a result, business management is learning about how best to harness the data. Such is the reason why analysts are in such high demand. Their ability to make good use of data makes them indispensable for most business owners. It’s also the reason why artificial intelligence is so successful. It can help streamline so many processes, including video annotation, workforce management, and so much more. Understanding AI and accessibilityOne of the reasons why AI is useful, despite being a form of emerging technology, is that it can help deal with human error. For example, a company can use AI transcription services to help with closed captions for their video content. Not only does it help gather the support of those who benefit from closed captions in videos, but it also offers a means for search algorithms to index content. Adding a transcript to videos is a form of search engine optimization (SEO), allowing even the most inexperienced company owner to benefit from robust marketing strategies. Those who are looking to make their company more accessible will naturally stand to benefit from what AI has to offer. It also helps that accessibility is one of the keys to a successful business venture. It’s easy to support a company that takes the time to be more accessible to its target demographic. It also has the added benefit of taking human error out of the picture. While it might take some time for mainstream AI to master accurate transcription, there are many services that offer accurate transcription services for an affordable price range. The subtle impact of customer serviceFor startups, it can be quite challenging to adopt a meaningful customer service system without outsourcing. It's quite similar to IT managed services, where it's best for newer companies to get the help of professionals to get the job done. The good news is that most types of customer service roles can be filled by artificial intelligence. In addition, there are many professional services available that offer a full system for customer service without effort. Quality customer service matters, which is why most newer companies are recommended to keep an active social media account no matter the situation. If users are happy with the customer service, it can go a long way to boosting a new company’s overall popularity. Creative and core tasksMany company owners have a hard time expanding their reach because creative and core tasks take a secondary role in keeping the company afloat. As a result, most company owners and their staff are stuck performing the most tedious tasks to get the job done, which doesn't free up time and resources to do anything else. The dawn of AI allows companies to use AI services for the most tedious processes, giving the creatives of your company the freedom to do what they do best. The future of AIOne of the most exciting parts of AI is that it’s still considered an emerging technology. What it means is everything artificial intelligence can do today is only the tip of the iceberg. There is so much more that AI could potentially accomplish, and it’s bound to improve as time goes by. Larger companies are typically best-equipped to handle AI, but there will come a time when even the smallest company can use quality AI services to deal with various issues. Artificial intelligence is a fascinating subject and one that undoubtedly impacts companies no matter their size. Even brand new companies have a chance at success with the help of emerging tech such as AI. All you have to do is look at what's trending to figure out the best tools for your business.

You've probably already heard about how artificial intelligence (AI) is slowly but surely sweeping the business sector and making things easier for large …

With the use of Machine Learning (ML) on the rise, it is more important than ever to take a look at the leading five ML libraries being used today. But before we get into that, let’s look at what is an ML library? A Machine Learning library, or a Machine Learning framework, is a set of routines and functions that are written in a given programming language. Essentially, they are interfaces, libraries or tools helping developers to easily and quickly build machine learning models, going past the specific basic details of the underlying algorithms. So they basically help developers carry out complex tasks without having to rewrite many lines of code. Now let us look at the five best ML libraries out there for developers today: 1. TensorFlow Created by the Google Brain team, TensorFlow is a free and open-source software library used for research and production. Allowing easy and effective implementation of machine learning algorithms, it is an efficient math library and is also used for machine learning applications such as neural networks. The emergence of high-level APIs (Application Programming Interfaces) like Keras and Theano has made TensorFlow more effective in improving the capability of computers to predict solutions with a greater degree of accuracy. Bear in mind that TensorFlow offers stable APIs for Python and C. Providing parallel processing, it is easily trainable on CPU as well as GPU (Graphics Processing Unit) for distributed computing and enjoys a large community support with additional advantages including better computational graph visualizations, quick updates, frequent new releases with new features, good debugging methods and scalability. 2. Keras Several back-end neural network computation engines are supported by Keras, an open-source neural network library written in Python. It can run on top of frameworks such as TensorFlow, Microsoft Cognitive Toolkit, Theano. Keras has many impressive features. First is modularity, where a model can be understood as a sequence or a graph alone, next is minimalism so that the library shares just enough to get an outcome and there’s also the element of maximizing readability and extensibility which allows researchers to do more trials. Its advantages include its support for a wide range of production deployment options and integration with back-end engines/frameworks; it also helps that everything in Keras is native Python. Kids and teens interested in learning TensorFlow and Keras can join the YoungWonks afterschool coding program. Here, they will first get to learn the basics of Python and work their way up to learn about the two ML libraries in live online classroom sessions focusing on project-based and self-paced learning. 3. Scikit-learn Scikit-learn is a free machine learning library for Python built on SciPy. An effective tool for data mining and data analysis, it is used today for model selection, clustering, preprocessing, and more. Its popularity can be traced to the fact that it boasts a clean API, is easy to use, fast, comprehensive and enjoys good documentation and the support of an active developer community. It also scores well on the simplicity and accessibility front. 4. Theano Also a Python ML library, Theano is an open-source project developed by Montreal Institute for Learning Algorithms (MILA) at the Université de Montréal. It allows developers to define, optimize and evaluate mathematical expressions that include multi-dimensional arrays. It provides features such as good integration with NumPy, transparent use of a GPU, extensive unit-testing, and self-verification. 5. PyTorch Developed by Facebook’s AI Research lab (FAIR), PyTorch is used for applications like computer vision and natural language processing. Also a free and open-source software, it has a polished Python interface along with a C++ interface. PyTorch offers Tensor computing (like NumPy) with strong acceleration via graphics processing units (GPU) and today many deep learning softwares have been/ are being built on top of PyTorch.

Technology may shape the way people live, but sometimes the inverse is also true. In many ways, the issues of 2020 will drive the innovations of 2021, especially in AI. You can determine what AI research and development will look like by looking at where it needs to go from here. AI expert Mark Gorenberg predicts that the pandemic will spur an AI revolution, just as the Great Recession did for big data. As the outbreak and subsequent recession reveal the world's shortcomings, AI will rise to fix them. The next generation of AI will be one that addresses the problems of today. Medical Research If the pandemic has highlighted one area of need, it's the health and medicine sector. Healthcare systems need better tools to be able to predict and respond to any outbreaks in the future. The predictive power of AI offers a solution. Researchers are already using AI to find drugs that could potentially fight COVID-19. A National Science Foundation-funded supercomputer program is using machine learning to run simulations about how the virus interacts with different compounds. Using the results from these simulations, scientists could find potential vaccines to start testing. As people recognize the value of systems like these, it will lead to further research and development. In the coming years, you'll see a broader emphasis on healthcare technologies in the AI industry. AI could help scientists predict and prevent outbreaks or treat them faster if they do occur. Navigating New Data Regulations As big data becomes more of standard practice, data governance is a more pressing concern. People are becoming more aware of how companies are gathering their information, which will likely lead to more data regulations. In response, companies will turn to AI to ensure they don't violate any privacy laws with their data use. AI solutions can help institutions balance convenience for their customers with privacy and security. With the help of AI, organizations like banks can manage data across multiple platforms, keeping customer information safe while still making it accessible. Handling these things manually could make it more challenging to stay within increasing guidelines. Automating data management will become increasingly critical to businesses as both data and regulations grow. AI that can understand and follow restrictions like the GDPR will become a necessity. National Security The past couple of years have also brought new emphasis to the importance of cybersecurity. AI in cybersecurity is nothing new, and many organizations employ it already, but you don't see it on a national level. As cyber-risks have become more prominent, though, AI will play a more significant role in national security. Government adoption of technology is typically slower than that you see in the private sector. AI has already established itself in the commercial world, so the logical next step is the government. For national security agencies to implement these technologies, though, AI will have to prove its reliability and security. Governments in the U.S. and Europe experienced several cyber attacks from threat actors like North Korea this year. To combat these rising threats, agencies will have to turn to AI. As a result, cybersecurity AI will evolve rapidly over the next few years. AI in the IoT The convergence of separate technologies is a natural step in development. One of the most noteworthy you'll see in the future is the marriage of AI and the IoT. As the IoT grows, so will AI functionality in these devices. The IoT is a provides AI with the landscape necessary for it to see wider adoption and implementation. AI technologies like self-driving cars will need to take advantage of edge computing, which requires the IoT. AI development in the coming years will shift towards IoT platforms. AI-enabled IoT devices will also make smart cities a possibility. In the face of growing environmental and sociological concerns, that's a needed improvement. You can already see this trend starting to take place, and it will only increase from here. An AI Revolution Is Coming Soon The world stands on the cusp on a technological revolution. AI may not be new technology, but it's still growing, changing, and driving innovation. In the next few years, AI will see much wider adoption and an unprecedented period of advancement. Technological shifts typically follow significant cultural or societal events. The tumultuous period that has been 2020 will spur the next wave of AI technologies.

Last week ROKO Labs had our unofficial launch for InstaBot at New York TechDay. Our team put together a great InstaBot presentation and gave away some bot related swag. We also met a lot of engaging people from all sorts of companies. Naturally we were there to talk to anyone interested in chatbots and how InstaBot could solve all of their engagement problems. We received a lot of interesting questions about use cases, implementation, and the conversation building experience.