Abstract: AI-assisted pipeline copilot techniques are described herein. In one example, a workflow method using an AI-assisted pipeline copilot involves receiving pipeline information from a user for an artificial intelligence (AI) pipeline and identifying key words in the pipeline information. A recommended next task component to add to the AI pipeline is then determined using a neural network model based on: a mapping of the key words to AI pipeline stages and one or more previous task components added to the AI pipeline. Connections between the recommended next task and the existing pipeline can also be inferred with a second neural network model. The recommended next task components and connections can then be provided to the user (e.g., with a graphical user interface).

Abstract: Processes and systems are disclosed. The processes and systems are arranged to apply convolution for a CNN where the CNN is simplified using sparse techniques, quantization techniques or both sparse and quantization techniques. A location vector (LV) table is provided to record the coordinates of non-zero weights. A look up table is provided to recover the real weight value from the weight identification. Convolution is applied by retrieving the coordinates of the next non-zero weight and the associated real weight value and by accumulating the multiplication of the real weight value and the input value across the input activation plane.

Abstract: Embodiments of apparatuses and methods for adaptive data compression and associated contextual information are described. In various embodiments, an apparatus may include a context monitoring module to gather contextual information for transmission of data and a policy module to gather user preference on cost associated with transmission of data. The apparatus may further include an analysis module to determine whether to compress data prior to transmission, based at least in part on the contextual information and the user preference. Other embodiments may be described and/or claimed.

Abstract: Embodiments of apparatuses and methods for adaptive data compression and associated contextual information are described. In various embodiments, an apparatus may include a context monitoring module to gather contextual information for transmission of data and a policy module to gather user preference on cost associated with transmission of data. The apparatus may further include an analysis module to determine whether to compress data prior to transmission, based at least in part on the contextual information and the user preference. Other embodiments may be described and/or claimed.

Abstract: This is a peer-to-peer system for controlling and restricting access (reading, writing, creating, deleting, manipulating, and control) to data and data representations of processing engines through the use of secure containers, an access processing engine, and cryptographic keys.

Abstract: This is a system for controlling and restricting access (reading, writing, creating, deleting, manipulating, and control) to data and data representations of arbitrary processing engines through the use of secure containers, an access processing engine, and cryptographic keys.

Abstract: Asset management systems and methods are presented. In one embodiment, a system includes a computing resource associated with a project member. A project container is stored on the computing resource, wherein the project container comprises encrypted objects related to a project. The encrypted objects includes project metadata and one or more working objects associated with one or more sub-projects of which the project member is granted permissioned access. An encryption/decryption engine is included for encrypting and decrypting the encrypted objects. The system includes an archive file system for storing the encrypted objects and previous versions of the objects, and a façade file system for viewing and accessing and interacting with the one or more working objects. Other computing resources associated with other project members are similarly configured, wherein a plurality of project containers store distributed objects that are grouped within the project.

Abstract: This is a system for controlling and restricting access (reading, writing, creating, deleting, manipulating, and control) to data and data representations of arbitrary processing engines through the use of secure containers, an access processing engine, and cryptographic keys.

Abstract: Asset management systems and methods are presented. In one embodiment, a system includes a computing resource associated with a project member. A project container is stored on the computing resource, wherein the project container comprises encrypted objects related to a project. The encrypted objects includes project metadata and one or more working objects associated with one or more sub-projects of which the project member is granted permissioned access. An encryption/decryption engine is included for encrypting and decrypting the encrypted objects. The system includes an archive file system for storing the encrypted objects and previous versions of the objects, and a façade file system for viewing and accessing and interacting with the one or more working objects. Other computing resources associated with other project members are similarly configured, wherein a plurality of project containers store distributed objects that are grouped within the project.