FIVE MINUTES WITH ...
How do you structure your organisation to really harness this potential ? Such change begins from the top , at the C-level . Engagement , ownership , governance , and investment into centres of excellence . These are the keys to fostering an AI-driven culture throughout the organisation . A means to make the firm not just a user of AI to start off with , but – in the end – a firm that has been totally transformed by AI . In effect , an ‘ AI-native ’ organisation .
Q . Tell me about Dataflow-as-a-Service – how does it support customers and improve their operations ?
» Dataflow-as-a-Service is an evolution of SambaNova ’ s product offerings . It supports customers ideally as it has been 100 % driven by customer feedback since the first iteration . A key lesson we have taken from our customers is that many are moving from model-centric computing to data-centric computing . Dataflow-as-a- Service enables end-users to engage with AI computing from a data-centric standpoint . It provides the machine learning model and the underlying infrastructure , right-sized , and made available in an applicationfriendly , accessible way through APIs . Gone are the days when end-users need years of experience developing machine learning models . SambaNova selects and provides the optimal model for the use cases that the customer needs . We make these models accessible through said APIs and ensure they can be plugged in effortlessly at the application level .
This leaves the end-user customer to focus on their areas of expertise : their dataset and their applications . The rest of AI transformations ’ complexity can be left to SambaNova , provided to them as-a-Service .
Q . How have you seen AI and ML transform the way you work ?
» There are two trends happening right now that are transforming how AI is being used in organisations , which is ultimately changing how we work .
The first is the growth of multiple model deployments . This is the convergence of different types of AI models within a single pipeline – one well-known example is the combination of natural language processing ( NLP ) and computer vision that has resulted in OpenAI ’ s DALL-E 2 .
Another more practical multiple model example is in language models pulling out anomalies in a text-log that are subsequently fed into a recommendation algorithm . We all know recommendation engines from the ‘ you bought this , perhaps you ’ d like this ’ use case in ecommerce , but in the context of an NLP model , it can be leveraged to provide a recommendation of the next best action to a support analyst when remediating the anomaly seen in the text log .
“ AI has the potential to reshape the Fortune 500 , just like the internet did . Established , decadesold players could fall away while unknown , disruptive challengers could rise and become the next leaders of industries ”
22 February 2023