Where were you when artificial intelligence (AI) came online? Remember that science fiction movie where AI takes over in a near dystopian future? The plot revolves around a crazy scientist who accidentally put AI online, only to realize the mistake too late. Soon the machines became the human’s overlords. While these science fiction scenarios are entertaining, they really just stoke fear and add to the confusion to AI. What enterprises should be worried about regarding AI, is understanding how their competition is embracing it to get a leg up.
Where were you when your competition put Artificial Intelligence online?
Implementations of artificial intelligence with Natural Language Processing is changing the way enterprises interact with customers and conduct customer calls. Organizations are also embracing another form artificial intelligence called computer vision that is changing the way Doctors read MRIs and the transportation industry. It’s clear that artificial intelligence and deep learning are making an impact in the enterprise. If you are feeling behind, no problem, let’s walk through strategies enterprises are embracing for implementing AI in their organizations.
The first step to embracing AI into your organization is to define an AI strategy. Jack Welch said it best “In reality, strategy is actually very straightforward. You pick a general direction and implement like hell.” Designing a strategy starts with understanding the business value that AI will bring into the enterprise. For example, a hospital might have an AI initiative to reduce the time necessary to recognize patients experiencing a stroke from CT scans. Reducing that time by minutes or hours could help get critical care to patients and ultimately deliver better patient outcomes. By narrowing and defining a strategy, Data Scientists and Data Engineers now have a goal to focus on achieving.
Once you have a strategy in mind, the most important factor in the success of artificial intelligence projects is the data. Successful AI models cannot be built without it. Data is an organizations number one competitive advantage. In fact, AI and deep learning love big data. An artificial intelligence model that helps detect Parkinson’s disease must be trained with considerable amounts of data. If data is the most critical factor, then architecting proper data pipelines is paramount. Enterprises must embrace scaled out architectures that break down data silos and provide flexibility to expand based on the performance needs of the workload. Only with scale-out architectures can Data Engineers help unlock the potential in data.
After ensuring data pipelines are architected with a scale-out solution, it is time to fail quickly. YES! Data Scientists and Data Engineers have permission to fail but in a smart fashion. Successful Data Science teams embracing AI have learned how to fail quickly. Leveraging GPU processing allows Data Scientists to build AI models faster than anytime in human history. To speed up the development process though failures, solutions should incorporate GPUs or accelerated compute. Not every model will end with success, but will lead Data Scientists closer to the solution. Ever watch a small child when they are first learning how to walk? Learning to walk is a natural practice of trial and error. If the child waits until he/she has all the information and the perfect environment, they may never learn to walk. However, that child doesn’t learn to walk on a balance beam, it starts in a controlled environment where she can fail. A Data Science team’s start in AI should take the same approach, embracing trial and error while capturing data from failures and successes to iterate into the next cycle quickly.
The journey may seem overwhelming. However, those forward-thinking enterprises who take on the challenge in AI will gain market share. Dell Technologies is perfectly placed to guide customers through their AI journey with services to help with an Artificial Intelligence strategy, to the industry leading AI solutions like the Dell EMC Ready Solutions for AI and Reference Architectures for AI. These AI solutions give you informed choice and flexibility on how you deliver NVIDIA GPU accelerated compute complemented by Dell EMC Isilon’s high performance, high bandwidth scale-out storage solution which simplifies data management for training the most complex deep learning models.
Where were you when your competition put Artificial Intelligence online?
Artificial Intelligence in the Enterprise
Implementations of artificial intelligence with Natural Language Processing is changing the way enterprises interact with customers and conduct customer calls. Organizations are also embracing another form artificial intelligence called computer vision that is changing the way Doctors read MRIs and the transportation industry. It’s clear that artificial intelligence and deep learning are making an impact in the enterprise. If you are feeling behind, no problem, let’s walk through strategies enterprises are embracing for implementing AI in their organizations.
Key Strategies for Enterprise AI
The first step to embracing AI into your organization is to define an AI strategy. Jack Welch said it best “In reality, strategy is actually very straightforward. You pick a general direction and implement like hell.” Designing a strategy starts with understanding the business value that AI will bring into the enterprise. For example, a hospital might have an AI initiative to reduce the time necessary to recognize patients experiencing a stroke from CT scans. Reducing that time by minutes or hours could help get critical care to patients and ultimately deliver better patient outcomes. By narrowing and defining a strategy, Data Scientists and Data Engineers now have a goal to focus on achieving.
Once you have a strategy in mind, the most important factor in the success of artificial intelligence projects is the data. Successful AI models cannot be built without it. Data is an organizations number one competitive advantage. In fact, AI and deep learning love big data. An artificial intelligence model that helps detect Parkinson’s disease must be trained with considerable amounts of data. If data is the most critical factor, then architecting proper data pipelines is paramount. Enterprises must embrace scaled out architectures that break down data silos and provide flexibility to expand based on the performance needs of the workload. Only with scale-out architectures can Data Engineers help unlock the potential in data.
After ensuring data pipelines are architected with a scale-out solution, it is time to fail quickly. YES! Data Scientists and Data Engineers have permission to fail but in a smart fashion. Successful Data Science teams embracing AI have learned how to fail quickly. Leveraging GPU processing allows Data Scientists to build AI models faster than anytime in human history. To speed up the development process though failures, solutions should incorporate GPUs or accelerated compute. Not every model will end with success, but will lead Data Scientists closer to the solution. Ever watch a small child when they are first learning how to walk? Learning to walk is a natural practice of trial and error. If the child waits until he/she has all the information and the perfect environment, they may never learn to walk. However, that child doesn’t learn to walk on a balance beam, it starts in a controlled environment where she can fail. A Data Science team’s start in AI should take the same approach, embracing trial and error while capturing data from failures and successes to iterate into the next cycle quickly.
Dell Technologies AI Ready
The journey may seem overwhelming. However, those forward-thinking enterprises who take on the challenge in AI will gain market share. Dell Technologies is perfectly placed to guide customers through their AI journey with services to help with an Artificial Intelligence strategy, to the industry leading AI solutions like the Dell EMC Ready Solutions for AI and Reference Architectures for AI. These AI solutions give you informed choice and flexibility on how you deliver NVIDIA GPU accelerated compute complemented by Dell EMC Isilon’s high performance, high bandwidth scale-out storage solution which simplifies data management for training the most complex deep learning models.
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