Adobe takes home the award thanks to its new, exciting update to Premiere Pro: text-based editing. At NAB, Adobe showed us why Premiere Pro is the go-to editing software for so many editors. While text-based editing was the highlight for us, Adobe also unveiled an impressive range of new features across its Creative Cloud video programs. Adobe showcased new features in Premiere Pro that will be shipping in May. These included text-based editing along with an AI-based workflow powered by Adobe Sensei.

A recent article by Ronald Brachman (Brachman, 1985) points out some philosophical or semantic problems in using the notion of a prototype, which is described by using default properties. The problem arises since default properties can be overridden or cancelled in representing particular instances, and therefore lack definitional power: i.e., they are not really essential to the concept being represented. As an example, Brachman presents an elephant joke: Q: What's big and gray, has a trunk, and lives in the trees? A: An elephant-I lied about the trees. Before discussing a solution to this dilemma, consider the following modified version of the elephant joke, perhaps not quite as funny: Q: What's big and gray, has a trunk, and lives in the trees?

Department of Computer Science, Columbia University, New York, NY 10027 Abstract This article surveys a portion of the field of natural language processing. The main areas considered are those dealing with representation schemes, particularly work on physical object representation, and generalization processes driven by natural language understanding The emphasis of this article is on conceptual representation of objects based on the semantic interpretation of natural language input. Six programs serve as case studies for guiding the course of the article. Within the framework of describing each of these programs, several other programs, ideas, and theories that are relevant to the program in focus are presented. RECENT ADVANCES in natural language processing [NLP] have generated considerable interest within the Artificial Intelligence [AI] and Cognitive Science communities.

In this article, principles involving the intrinsic, deictic, and extrinsic use of spatial prepositions are examined from linguistic, psychological, and AI approaches. First, I define some important terms. Second, those prepositions which permit intrinsic, deictic, and extrinsic use are specified. Third, I examine how the frame of reference is determined for all three cases. Fourth, I look at ambiguities in the use of prepositions and how they can be resolved.

An adequate natural language description of developments in a real-world scene can be taken as proof of "understanding what is going on." An algorithmic system that generates natural language descriptions from video recordings of road traffic scenes can be said to "understand" its input to the extent that algorithmically generated text is acceptable to the humans judging it. The ability to present a "variant formulation" without distorting the essential parts of the original message is taken as a cue that these essentials have been "understood." During art lessons, in particular those concerned with classical or ecclesiastic paintings, students are initially invited to merely describe what they see. Frequently, considerable a priori knowledge about ancient mythology or biblical traditions is required to succinctly characterize the depicted scene. Lack of the corresponding knowledge about other cultures can make it difficult for someone with only a European education to really understand and describe in an appropriate manner a painting by, for example, a Far East classic artist. Familiar human experiences mentioned in the preceding paragraph will now be "morphed" into a scientific challenge: to design and implement an algorithmic engine that generates an appropriate textual description of essential developments in a video sequence recorded from a real-world scene. Such an algorithmic engine will serve as one example of a cognitive vision system (CVS), which leaves room, as the experienced reader has noticed, for there to be more than one way to introduce the concept of a CVS. An alternative clearly consists in coupling a computer vision system with a robotic system of some kind and assessing the reactions of such a compound system. To whomever accepts the formulation, "one of the actions available to an agent is to produce language. This is called a speech act. Russell and Norvig (1995)" is unlikely to consider the two variants of a CVS alluded to previously as being fundamentally different. With regard to the first CVS version in particular, the following remarks are submitted for consideration: Obviously, we avoid a precise definition of understanding in favor of having humans compare the reaction of an algorithmic engine to that expected from a human. This fuzzy approach toward the circumscription of a CVS opens the road to constructive criticism--that is, to incremental system improvement--by pinpointing aspects of an output text that are not yet considered satisfactory.

What Is a Knowledge Representation? Although knowledge representation is one of the central and, in some ways, most familiar concepts in AI, the most fundamental question about it--What is it?--has Numerous papers have lobbied for one or another variety of representation, other papers have argued for various properties a representation should have, and still others have focused on properties that are important to the notion of representation in general. In this article, we go back to basics to address the question directly. We believe that the answer can best be understood in terms of five important and distinctly different roles that a representation plays, each of which places different and, at times, conflicting demands on the properties a representation should have.

This supposedly makes representing exceptions (three-legged elephants and the like) easy; but, alas, it makes one crucial type of representation impossiblethat of composite descriptions whose meanings are functions of the structure and interrelation of their parts. This article explores this and other ramifications of the emphasis on default properties and "typical" objects. While I believe this to be an important point, this article was never meant to be the definitive work on logical distinctions in knowledge representation. Some of the notions mentioned here in passing (e.g., analyticity) are perenially problematic. In addition, I have not really attempted to bring the body of the article up to date from its original form. The article is also generally nonconstructive. However, there is now ample evidence that this kind of analysis can lead to constructive suggestions for knowledge representation systems. In work pursued after the original version of this article was written, some ...

The recent history of expert systems, for example, highlights how constricting the brittleness and knowledge acquisition bottlenecks are. Moreover, standard software methodology (e.g., working from a detailed "spec") has proven of little use in AI, a field which by definition tackles ill-structured problems. How can these bottlenecks be widened? Attractive, elegant answers have included machine learning, automatic programming, and natural language understanding. But decades of work on such systems (Green et al., 1974; Lenat et al., 1983; Lenat & Brown, 1984; Schank & Abelson, 1977) have convinced us that each of these approaches has difficulty "scaling up" for want of a substantial base of real world knowledge.

The capabilities for representing and reasoning about three-dimensional (3-D) objects are essential for knowledgebased, 3-D photointerpretation systems that combine domain knowledge with image processing, as demonstrated by 3-D Mosaic and ACRONYM. Three-dimensional representation of objects is necessary for many additional applications, such as robot navigation and 3-D change detection. Geometric reasoning is especially important because geometric relationships between object parts are a rich source of domain knowledge. A practical framework for geometric representation and reasoning must incorporate projections between a two-dimensional (2-D) image and a 3-D scene, shape and surface properties of objects, and geometric and topological relationships between objects. In addition, it should allow easy modification and extension of the system's domain knowledge and be flexible enough to organize its reasoning efficiently to take advantage of the current available knowledge.

Humans perform visual recognition/detection and then predict motion of independent objects. The brain relies strongly on understanding of physical world and object geometry/affordances in estimating motion (and thus it's much more difficult), the "one model fits all" approach is completely wrong. Shouldn't the baseline be detection - motion estimation? If you are really interesting in solving the robotic arm problem why not just create a network that leverages ground truth information about location and motion of the arm.