Amazon.Com, Inc. / Syaza Kautsar Mohamad Halim
Bibliographic record
Abstract
Amazon.com, Inc. is an American multinational technology company which based in Seattle, Washington, which focuses on e-commerce, cloud computing, digital streaming, and artificial intelligence. Along with Google, Apple, Microsoft, and Facebook, Amazon is one of the Big firms in the United States' information technology industry. The business has been labelled "one of the most powerful economic and cultural forces in the world," as well as the most successful brand on the planet. In this study, product from Amazon that focus on is Alexa. Alexa is a virtual assistant AI (Artificial Intelligence) which capable to voice interaction for music playback, making to-do lists, setting alarms and many more. Alexa also can control several smart devices in home automation system.The first problem regarding Alexa is absence on mobile devices. As we know smartphone is necessary item in most of our daily life. Its better for Alexa to also focusing more on utilization fully of smartphone potential and fully use it. Google assistant and Siri can be example as a reference. Secondly, language option in Alexa. Currently, interaction and communication with Alexa are available only in English, German, French, Italian, Spanish, Portuguese, Japanese, and Hindi. In Canada, Alexa is available in English and French (with the Quebec accent). In order to meet every user requirement, the language option should be wider. Alexa also must improve and add on voice customize which enable user to customize Alexa voice by using human voice on its own either by user or someone else and make it less “robot” voice. Furthermore, Alexa also can improve by adding “Kids Mode”. Its will be help for parent to monitor their kid’s behaviour and online activities.Moreover, solution regarding problem state as before are needed to do innovation, Research and Development (R&D) and update software in Alexa. The next solutions are do survey among user and non-user regarding what Alexa need to improve. This is important because in order to meet user needs, we must know what they need. Developer team of Alexa application which already available in mobile platform should upgrade more usage especially in term of language option and voice customization. Kid’s mode also should be added in Alexa system to avoid kids do something out of parent supervision. All these solutions are considered as to improve Alexa in reborn into more quality virtual assistant AI technology to be served into customers which affordable with the price also help to sustain their business growth, developments and competitiveness in market with other big brands.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.578 | 0.647 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".