FeatureLast year, we saw increasing innovation around artificial intelligence-powered autonomous driving technology. The automotive vehicles’ capability to collect complex data from their surroundings and use machine learning to make precise and accurate driving decisions has improved exponentially.
A recent IDC report forecasts that the number of vehicles that will be capable of Level 1 autonomy will increase to 54.2 million units in 2024.
There has also been tremendous innovation in Natural Language Processing, a subset of artificial intelligence focused on training machines to understand human language. This escalated as the need for chatbots surged due to the pandemic.
Continuous innovation in the field has powered more accurate search results, enhanced applied NLP tasks like translations, question-answering and improved user experience.
Another promising innovation area around artificial intelligence and machine learning is the use of quantum computing. In the near future, we can expect quantum computing to significantly increase the capabilities of artificial intelligence and machine learning. Quantum computing will give machine learning the capability to create systems that execute multi-state operations simultaneously and tackle complex issues in a split second.
There has also been tremendous innovation in Natural Language Processing, this escalated as the need for chatbots surged due to the pandemic
The key challenge faced by the majority of datacentres today, in regard to the implementation of artificial intelligence and machine learning, is the lack of adequate technology that meets the performance needs of artificial intelligence and machine learning.
Most datacentres today are unable to match the performance required to effectively ingest, analyse, and manipulate data in real-time for artificial intelligence and machine learning operations.
Deploying artificial intelligence-ready infrastructure can help solve these issues. Additionally, by introducing graphics processing units into the datacentre, all these steps can be accelerated, which will result in an improved performance that is able to meet the needs of artificial intelligence and machine learning.
From an enterprise perspective, the most prominent challenge is the lack of access to clean, meaningful high-quality data. Enterprises must make sure they have a data architecture that consists of data preparation tools that are designed for data cleansing, formatting, and standardisation before storing their data in data lakes.
A lot of organisations today are still running on outdated legacy systems that make it challenging to implement artificial intelligence and machine learning. Therefore, before undertaking any artificial intelligence, machine learning project, enterprises must assess their IT infrastructure and make sure they have the proper foundations for the project.
From an enterprise perspective, the most prominent challenge is lack of access to meaningful high-quality data
Enterprises must also make sure their data is of high quality as this can derail the artificial intelligence, machine learning project. The data type, size and the artificial intelligence, machine learning use case NLP, computer vision, tabular will affect choice of hardware and accelerators.
Having a robust deployment plan is of high priority in any machine learning project as it makes testing and integration at different points smoother. Furthermore, there is also a need to create workflow automation throughout the phase of the project so teams can collaborate easily.
Measuring the ROI of an artificial intelligence, machine learning project can be quite tricky if not done from the start. It is usually related to generating new business revenue streams, reducing inefficiencies, or automating mundane and repetitive tasks.
Enterprises must implement disciplined tracking, monitoring, and measurement systems at every step of the artificial intelligence, machine learning project to get an accurate understanding of the ROI.
Furthermore, it is advisable that enterprises select problems that are easy to measure and make sure their cross-functional teams are trained and educated on all processes and operations.
Artificial intelligence and machine learning are highly versatile and can be deployed across a variety of industries. An industry where these technologies are making a huge impact is healthcare. Artificial intelligence has significantly enhanced digital consultation, enabled highly accurate robotic surgery, and has helped medical specialists make better-informed diagnoses of patients.
IDC forecasts that the number of vehicles capable of Level 1 autonomy will increase to 54.2 million units in 2024
A report by IDC found that 50% of healthcare organisations expect an increase in demand for artificial intelligence-based solutions post the pandemic. Furthermore, artificial intelligence has also enabled hassle-free data maintenance of Electronic Health Records. These were fueled by recent advancements in NLP algorithms and transfer learning which accelerated the adoption of these techniques and shortened the time to develop new use cases and deploy them into production.
Artificial intelligence and machine learning have proved to be of high significance in the education sector during the course of the pandemic. As artificial intelligence takes care of admin jobs and other time-consuming tasks such as grading, teachers have found the time and freedom to develop learning content and mentor students. Along with enabling virtual assistance and improving digital assessment systems, artificial intelligence has also made it possible to provide highly customised learning, while also offering recommendations for how to close gaps in learning.
Furthermore, artificial intelligence and machine learning have made a mark in the self-service industry by enabling advanced chatbot systems. Ever since their inception, backed around artificial intelligence and machine learning technologies, chatbots have been able to provide 24 x 7 support to customers, answer complex queries and even provide product recommendations to customers. A study by Gartner estimates that 70% of white-collar workers will be interacting with chatbots daily by 2022.
Dell Technologies has solutions for simplifying sourcing, deployment, and management of infrastructure designed for the data era and artificial intelligence use cases, ranging from the edge, to the core, to the cloud. These include the Dell Precision Workstations, Dell EMC PowerEdge Servers, Dell EMC PowerScale Storage, and Dell EMC Data Protection solutions.
Gartner estimates that 70% of white-collar workers will be interacting with chatbots daily by 2022
These artificial intelligence-ready infrastructures provide state-of-the-art capabilities that enable every organisation to have access to the transformative power of artificial intelligence. Furthermore, they provide the processing power, capacity, throughput, and scale to power the most advanced artificial intelligence projects.
They are flexible and massively scalable to handle all types of data including structured, unstructured, and semi-structured. Additionally, the Dell Precision Data Science Workstation, a fully integrated artificial intelligence hardware and software solution, delivers the power to deploy and manage cognitive technology platforms, including machine learning, artificial intelligence, and deep learning; giving data scientists powerful, yet flexible computing resource to extract valuable insights from vast amounts of data.
The Dell EMC Ready Solutions for artificial intelligence include everything organisations need to accelerate their artificial intelligence initiatives. Helping make artificial intelligence simpler, these pre-designed and pre-validated solutions are ideal for machine and deep learning so organisations can get faster, deeper insights into their customers and their businesses.
A lot of organisations are still running on outdated legacy systems with lack of quality data, making it challenging to implement AI and ML.
