19 November 2019
KBPedia
Labels:
big data
,
data science
,
linked data
,
natural language processing
,
ontology
,
semantic web
,
text analytics
9 November 2019
7 November 2019
6 November 2019
LIDA
LIDA
Models of Consciousness (Scholarpedia)
Models of Consciousness (Wikipedia)
Neuroscience
Neuroscience Online
Harvard Neuroscience Online
Models of Consciousness (Scholarpedia)
Models of Consciousness (Wikipedia)
Neuroscience
Neuroscience Online
Harvard Neuroscience Online
5 November 2019
What is AI
What is AI? | Thinking | Acting |
Humanly | Cognitive Science | Turing Test, Behaviorism |
Rationally | Laws of Thought | Doing The Right Thing |
3 November 2019
2 November 2019
Ladder Ontologies
- Asocial Ontologies
- Social Ontologies
- Cultural Ontologies
- Oral Linguistic Ontologies
- Literate Ontologies
- Civilization-scale Ontologies
Labels:
big data
,
data science
,
deep learning
,
linked data
,
machine learning
,
natural language processing
,
semantic web
,
text analytics
1 November 2019
Java Demise
The speed with which new versions are being released every year spells the end of Java in the practical business world in the foreseeable future. There are two release schedules each year (every 6 months) which is significant. The biggest hurdle for businesses is maintenance and resources. There are many products that are still dependent on Java 8 and while there is a requirement for commercial licenses for upgrades since 2019. The other being technical debt and backwards compatibility constraints especially when the product is implemented in Java and then sold to customers. In a very short span of time there have been quite a few changes to the language and an ample set of versions. One can say that the Java release cycle has exploded in speed that the majority of the community for all practical intents and purposes will not be able to keep up. What this also means is that the ecosystem of tools and libraries take a while to upgrade making it a frustration in management for the engineering and the support teams. The Java ecosystem is huge, the fall back mechanism with lots of boilerplate code, formal testing processes from lack of design patterns baked into the language, and dependency hell is a massive hurdle with the language. It seems like gradually more and more organizations will distance themselves away from Java in order to keep maintenance costs down, meet customer expectations and demand for new product features as well as to reduce complexity especially in mobile and cloud environments. Another susceptible reason is the Oracle ownership of the language and the expectations provided in terms of license for the end user. Unfortunately, there is a love hate relationship for the language in the community. Even if the language were to reduce in interest in the community, it would still appear as the underdog from under the covers and rear its ugly head as a dependency for other languages like Groovy, Kotlin, and several open source Microservices and Big Data platforms.
Labels:
big data
,
Cloud
,
data science
,
distributed systems
,
Java
,
mobile
,
programming
,
software engineering
XLNet
Labels:
big data
,
data science
,
deep learning
,
machine learning
,
natural language processing
,
text analytics
Subscribe to:
Posts
(
Atom
)