Artificial intelligence is fast becoming a vital component of the way that businesses operate and play a major role in key strategic decision making.
Intelligent business applications are now using data science and machine learning techniques for greater impact on speed of decision making, what useful data is and how to find and incorporate the new information.
The whole point of artificial intelligence in computer science is to make business operations easier and faster.
Here are some of the best tools that you should be looking into adopting into your enterprise.
Tensorflow is used for dataflow programming and machine learning applications for artificial neural networks (ANNs). It is a decentralised software development programme meaning that it is open to public participation between peers and is prominent in writing such as coding.
Tensorflow was developed by Google and it can run on a variety of different CPUs and GPUs. It is a mathematical library of computations and algorithms used for machine learning and these are expressed as dataflow graphs.
Tensorflow programmes are stateful ie the computations are designed to remember preceding events that a user has inputted to the system.
It is used as part of Google’s DeepDream program, which uses a convolutional neural network to find and enhance patterns in images.
In essence, it is a data visualisation technique. It is available to use in multiple languages such as Python API, C API, C++ and Java.
The benefits of using Tensorflow as an AI service are as follows:
It allows automatic function differentiation
Tensorflow is able to use differentiation techniques to analyse and calculate the derivative of an inputted function. It has the capability to differentiate automatically and present the data with different visualisation methods.
Examples include dataflow graphs to make the data easier to understand and analyse.
Tensorflow also allows a user to define the underlying architecture of an algorithm.
It runs with optimal performance, regardless of your supporting hardware
Tensorflow allows asynchronous operations meaning that when it runs a series of programs, it does not have to wait for results in order to process other events outside of the defined originals.
It is able to be programmed in a variety of different languages such as Python and C++, meaning that you can deploy a model to run a computation in the most common styles.
It has a flexible architecture
Tensorflow provides the user with the ability to draw up a variety of different versions of the same model and run the algorithms at the same time.
Further to this, Tensorflow has been designed so that internal API is consistent, meaning that any migration to a previous version is possible but the API will not break when doing so.
It has great portability
As previously mentioned, Tensorflow can run on a number of different hardware systems. You can use on desktops, laptops, GPUs, CPUs and even on sufficient mobile platforms.
As part of its portability feature, you can even deploy a live model directly to your system. You don’t need a series of other supporting hardware to use Tensorflow when on the go.
Similar to Tensorflow, Keras is another open source neural network library but specifically written in Python. It is able to run on top of Tensorflow and designed to operate deep learning method.
The main purpose of Keras is to provide fast experimentation with deep neural networks (DNNs).
Like Tensorflow, Keras is very portable and can used on a variety of platforms including GPUs, on smartphones running on iOS and Android and also the Raspberry Pi.
There are plenty of advantages to using Keras:
It is easy to use
Keras is designed to provide consistent and simple APIs that are easy to follow.
It reduces the number of actions needed to complete a process but if there are any errors in an algorithm, Keras gives clear and precise feedback in how to solve and overcome the problems.
Keras enables you to use your time more efficiently and provide solutions to problems quickly using its DNNs.
It can integrate lower-level deep learning languages
Even though Keras is an easy AI tool to use, it remains very flexible in that it is simple to translate algorithms or computations built in one language into a system that is built in another.
Since Keras runs on top of Tensorflow, the Keras API can comfortably accommodate Tensorflow’s dataflow programming.
It supports multiple backend engines
When you develop computational models using Keras, there are many different data access layers that can be accessed, such as the Tensorflow backend.
Models that you develop can be learned on many different hardware platforms that go beyond the CPU level. Keras has built in support for multi-GPU data parallelism, meaning that it can focus in distributing data across different nodes which operate on the data in parallel.
Another AI tool to use in your business is robotic process automation (RPA). This is an emerging form of AI. Where traditional programs require human interaction in order to produce a set of instructions to carry out a task, RPA expands on the user inputs and then as part of the automation, repeats the actions straight into the graphical user interface (GUI).
RPA has similar processes to tools that specialise in testing a product’s GUI to ensure that it meets a defined set of specifications, but differs in that RPA tools can handle multiple sets of data to be actioned across multiple platforms simultaneously.
Once RPA systems are programmed to understand a process, it can communicate with associated systems accordingly.
The advantages of incorporating RPA programmes into your business are as follows:
Save on valuable resources
Historically, the cost of moving jobs from one location to another has been an effective method of saving on the cost of employment.
This has typically meant that business operations are taken to an offshore region. More often than not, it is more cost efficient to run certain aspects abroad rather than in your local domain.
The use of RPA is the next chapter; where previously you would need to hire someone to perform tasks, RPA allows a cheaper alternative in that a robot can perform these tasks for you. This will save you not only money but also valuable time.
RPA is easier to scale
Following on from the saving on resources, RPA has the ability to scale a lot quicker than by hiring now employees are moving operations to another location.
A new employee can take a lot of time bedding in to learning process systems and applications whereas an RPA can be deployed straight away.
Once that specific RPA is set, you can expect to see results quicker meaning that you will be able to grow your business more efficiently than before; your business will not need to be held back by human resources.
RPA programmes will always be able to operate in the same way in order to complete the task.
Human input will often result in different methods in order to achieve a task, especially if more than a single person will be working on that job, meaning that end results can become inconsistent.
RPA eliminates this as once they are programmed to operate in a certain way, they will not change and will have precise and accurate results.
Question answering systems
The final AI system that you should you to be using into your business is a question answering program such as Watson. Developed by IBM, Watson is a computer system that is able to answer questions that are composed by natural language processing.
The computer system was initially developed to answer questions on the quiz show Jeopardy! and, in 2011, the Watson computer system competed on the show against champions Brad Rutter and Ken Jennings, ultimately winning the first place prize of $1 million.
Watson parses questions into different keywords and sentence fragments in order to find statistically related phrases.
It’s main innovation was not that is is able to create a brand new algorithm to answer the question, but rather that is able to execute many tested and proven language analysis algorithms at the same time.
Watson is more likely to provide the correct answer to a question based on the more independent algorithms that find the same answer.
Once Watson has collected a small number of solutions that could potentially solve the problem, it checks the answers against its database to ascertain whether the solution makes sense or not.
Watson has been implemented into many fields already meaning that there an abundance of advantages of using.
The finance sector
In the financial sector, Watson can use its questioning and answering capabilities to provide financial advice and management. It is able to advise on potential risk of lending to a customer.
Watson is also being used in customer service applications in order to give them their most preferable method of contact. It decides whether it should be via phone, online web chat or even in person. IBM say that USAA was one of the initial firms looking for technology.
Watson is also provide assistance in wealth management in order to provide sound advice by identifying trends in markets and relaying it to customers.
The health sector
Watson is able to use inputted data about a customer and provide solutions to their needs. This is an advantage that can be applied to any business or organisation.
Specifically, Watson is having a huge impact in the health sector. It is now being used in some of the best cancer treatment hospitals in the United States. Hospitals such as Memorial Sloan Kettering Cancer Center and University of Texas MD Anderson Cancer Center.
In terms of cancer research itself, Watson is speeding up DNA analysis in cancer patients to help make their treatment more effective.
Watson is also able to provide doctors and physicians with providing correct and accurate patient diagnoses. A dermatology app called schEMA allows doctors to input patient data. Using natural-language processing (NLP), it helps identify potential symptoms and treatments.
Additionally, Watson uses vision recognition to help doctors read scans such as X-rays and MRI scans.
The retail sector
North Face, the outdoor and activewear giant, has partnered with IBM’s Watson. Their aim is to create an app that is based around finding the right clothing specifically for the customer.
In essence the app works like an online personal shopper to create a much more personalised shopping experience.
AI is being used to solve the problem of bridging the gap between purchasing products online or instore.
They can take on board what a customer is looking for, asking questions to narrow down potential solutions.