About
Smart Systems, Inc.
Smart Systems, Inc. (SSI) is a global leader in Artificial Intelligence specializing in areas such as Natural Language Processing, Computer Vision, Speech Recognition and Synthesis, Image Processing, and Robotics. We help clients transform their business operations by leveraging machine learning and deep neural networks to convert data into actionable predictions that are immediately impactful. Clients that are confounded by the complexities of AI technologies and platforms frequently engage us to lend clarity and inform the investment decision process. Our AI Opportunity Analysis Framework consists of four exceptional methodologies that orchestrate the identification and selection of the most promising AI solutions and has attained a stellar track record of repeated success. These competencies are complemented by an assembly of highly skilled business and technology practitioners driven by their passion for innovation and creativity and are readily available to serve as your AI partner.AI Framework
SSI has traditionally concentrated its AI efforts on Deep Learning, which has proven to be the most successful and promising approach to AI development. This approach attempts to mimic the way the human brain works, and as a result, has produced some of the most successful AI applications to date. While there are other methods of AI development that have their own advantages, such as evolutionary algorithms, they have yet to achieve the same level of success as Deep Learning. This is likely due to the fact that evolutionary algorithms are not as well-known or understood by the general public, and are more likely to result in applications that are not as reliable or effective. The demand for AI driven applications is on the rise, as businesses seek to capitalize on the unique abilities these applications provide. Businesses of all sizes are racing to implement AI driven applications in order to gain an edge on their competition. The proliferation of AI across a wide range of businesses has created a market that is unique in terms of the types of applications that are being created. This demand has created a market for AI driven application development that was not previously seen. SSI has seen a trend in its Artificial Intelligence offerings over the past few years, evolving from a niche practice to an all-encompassing one.| Items | Phases | Descriptions | Technologies |
|---|---|---|---|
| 1 |  | In order to implement an AI solution, it is important to first develop a concept for its intended usage. This concept should list the key characteristics of the AI solution, as well as how it will be implemented. | Apache Spark, Matlab, SAS, KNIME, Apache Flink, Rapidminer, DataRobot, Apache Hadoop, Microsoft Power BI, QlikView |
| 2 |  | The operating parameters, data sources, algorithms, and target model for the AI implementation must be defined. These components are crucial for the successful development and deployment of AI applications. Without a well-defined plan, it is difficult to create an AI system that meets all the requirements. | Convolutional Neural Network, Image Classification, Multiclass classification, Supervised learning, Kaggle, Talk Prediction, Object Recognition, Prediction, Hyperparameter |
| 3 |  | In order to create an AI product that is efficient and effective, you must first select the appropriate computational model. Once you have chosen the model, you will need to implement algorithms that will enable the product to operate optimally. | Armadillo, FANN, Keras, Matplotlib, mlpack, NLTK, NumPy, OpenNN, Pandas, PyTorch, Scikit-Learn, SciPy, Shogun, TensorFlow, Theano, H20: Open Source AI Platform, Google ML Kit, CNTK, Auto ML, Caffe, MxNet |
| 4 |  To train your chosen model, it is important to incorporate both positive and negative examples. By doing so, you can fine tune the AI to increase accuracy. Keep in mind that even small changes can have a big impact on the overall performance of the system. | Numpy Array, Class Imbalance, One Hot Encoding, Python, Tensorflow, Streamlit, Machine Vision, Convolutional Neural Network, Hyper Parameter, Data set
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| 5 | 
| Utilize previously unreported examples to test the trained AI model and confirm the accuracy of the predicted results with the anticipated outcomes. By doing this, you can be sure that the AI model is working as it should and that the results it produces are reliable. | Python, IBM Watson Cloud, Heroku, Jupyter, Streamlit, IBM cloud service, JSON, Java Script |
| 6 | 
| Assuming you have already implemented a model and completed testing, training it for production use is the next step. This will ensure that the model works as intended when used by internal staff or customers. Making sure that the model is effective in production use is crucial for meeting customer needs and maintaining a good reputation. | Amazon Web Services, Microsoft Azure, Google Cloud Platform, IBM Cloud, Nutanix, Oracle Cloud |
Many experts believe that by 2030, artificial intelligence (AI) will have transformed business in a number of ways. By increasing efficiency and automating tasks, businesses will be able to improve their overall performance. Additionally, AI can also help businesses to make better decisions by providing them with more accurate information. As such, it is clear that AI will have a significant impact on business by 2030.
Despite the rapid growth of AI in recent years, it is still in its early stages and its potential for transforming businesses has not yet been fully realized. While many businesses have begun to implement AI solutions, there are still many challenges that need to be addressed before AI can truly live up to its potential. When it comes to artificial intelligence (AI), we’ve only just scratched the surface of what this technology is capable of. In the coming years, AI will become even more integrated into our lives, with businesses across all industries looking to adopt AI in order to stay competitive. One area where AI is already making a big impact is in customer service. By 2030, it’s estimated that 85% of customer interactions will be handled by bots or virtual assistants.
In recent years, businesses have seen a rapid rise in the use of artificial intelligence (AI). AI holds great promise for companies looking to improve their operations and bottom line. However, implementation can be difficult and expensive. A study by PwC found that only 4% of businesses have fully deployed AI, while 44% are in the early stages of adoption. Looking ahead to 2030, it is clear that AI will continue to grow in importance for businesses around the world.
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SmartSystems has a diverse portfolio of deployable AI Models. We encourage you to request an AI Application Quotation / Demonstration in a Business Domain of immediate value to you.