How NC turn into MNC using AI/ML
Hello guys in this blog/story i’m going to discuss how NC(Normal companies) turn into MNC(Multi-National companies).
From companies like Amazon to Spotify and from apple to Netflix every major company whether tech/non-tech is dedicating resources to breakthroughs in artificial intelligence.
Personal assistants like Siri and Alexa have made AI a part of our daily lives.
Meanwhile, revolutionary breakthroughs like self-driving cars may not be the norm, but are certainly within reach.
TOP ARTIFICIAL INTELLIGENCE (AI) COMPANIES WITH $100M+ FUNDING
- Sift Science
As the big guys scramble to infuse their products with artificial intelligence, other companies are hard at work developing their own intelligent technology and services. Here are 34 artificial intelligence companies and AI startups you may not know today, but you will tomorrow.
The 10 tech companies that have invested the most money in AI
1. Google — $3.9 billion
2. Amazon — $871 million
3. Apple — $786 million
4. Intel — $776 million
5. Microsoft — $690 million
6. Uber — $680 million
7. Twitter — $629 million
8. AOL — $191.7 million
9. Facebook — $60 million
10. Salesforce — $32.8 million
I especially like this companies that hoe effectively they use AI/ML
Industry: Productivity, Writing
Location: San Francisco
What it does: Grammarly is an AI-enabled writing assistant that helps writers and communicators all over the world with spelling, grammar and conciseness. The browser plugin checks over content being written in real-time and alerts for everything from spelling mistakes to tone errors and even scans content for plagiarism. Grammarly works across multiple platforms, including Gmail, Slack, Jira, Medium and many more. All this with the help of AI.
Industry : Online cabs service
Location : India
What it does: Ola Cabs (stylised as OLΛ) is an Indian ridesharing company offering services that include vehicle for hire and food delivery. The company is based in Bangalore, Karnataka, India and was developed by ANI Technologies Pvt. Ltd. As of October 2019, Ola was valued at about $6.5 billion. A variety of venture capitalists including Softbank have large stakes in the company
The company claims that Ola Guardian is built on AI and machine learning capabilities on the Ola platform which enables it to continuously learn and evolve from millions of data points every single day to improve risk signaling and instant resolution
Location: New York City, U.S
What it does: Spotify is a Swedish-based audio streaming and media services provider, launched in October 2008. The platform is owned by Spotify AB, a publicly traded company on the New York Stock Exchange since 2018 through its holding company Spotify Technology S.A.. Spotify’s global headquarters are in Stockholm, Sweden
Spotify utilizes AI through their predictive recommendation engine which enables them to curate personalized playlists such as “Discovery Weekly” and “Release Radar.” The engine is built upon a combination of collaborative filtering, natural language processing and audio models to create a personalized list of thirty songs for each user. This type of recommendation engine creates value for artists who get more exposure to new users and makes customers stickier through increased satisfaction with the service. The recommendation will only become smarter over time as more and more data is fed into the ecosystem. Similar to the recommendation engine, Spotify is also experimenting with AI to facilitate the search process and streamline the user prompted discovery of new music.
1. Explore Page and Search Function
The whole idea of machine learning is that it’s far better about understanding those nuances than any algorithm has in the past, or than any single human being could,” Instagram co-founder and then-Instagram CEO Kevin Systrom
Through the support of tags and trending information, the users can find photos and posts on particular topics or activities, events, and also for exploring experiences, trending restaurants and places around the globe.
Now how does this function work?
Basically Instagram recognizes accounts that are more or less similar to one another by adopting a machine learning technique termed as “word embedding”. This technique deciphers the order in which words appear in the text in order to measure how connected they are. Instagram uses the same technique to decipher and comprehend how connected any two accounts are to each other. So in order to make its recommendations, the Explore system starts by observing the “seed accounts” which are the accounts the users have interacted with in the past by liking or saving their content. It then discovers the accounts that are similar to these and selects 500 pieces of content from them. These content pieces are then filtered in order to remove all spam, misleading and policy-violating content from them and then the remaining posts are ranked on the basis of how probable a user is to interact with each one. At long last, the top 25 posts are then sent to the first page of the user’s Explore tab.
2. Instagram bots
These bots are designed to automate the user’s account interactions. These do everything from liking comments that customers leave on posts, to posting comments on another person’s content. This is seen as an excellent way of increasing engagement. An example of an Instagram bot would be Kenji.AI.
This is a new Instagram bot that automates the user’s activity efficiently. It makes use of machine learning algorithms to comprehend who would be most likely to engage with your account.
(Speaking of bots, you can also sneak a peek at our blog on Chatbots)
3. Target Advertising
Instagram ensures it uses the big data it generates fully to its advantage by extracting and analyzing the customer insights it gains from it. Instagram sells advertising space to companies who happen to be interested in reaching a particular target audience and in sending out a particular marketing message by comprehending and understanding the search preferences and engagement insights of its users.
Being owned by a powerful tech giant like Facebook allows Instagram to have a vast network of insights and information for helping target advertising based on the audience’s likes, who they follow, and engage with and the posts they save.
4. Designing Personalised Feeds
With the level of content shared on the app rapidly growing it becomes more and more paramount for the platform to deliver content that is relevant to its users. Hence in 2016, Instagram altered its feed to display first, the posts it believes it’s users would favor and share instead of in reverse- chronological order.
To do this machine learning algorithm was put to work to go through all the content and carefully comprehend which of the content would be more relevant for its users, in order to design a personalized feed for each of them.
(Speaking of personalization, let’s take a look at what hyper-personalization is)
5. Dealing With Spam
With an abundance of content being shared every day across the app, some of it is bound to be spam.
Now how are these spam messages detected?
Instagram makes use of Artificial Intelligence’s text analytics algorithm “DeepText” for dealing with spam messages. Its spam filter can detect spam messages in over 9 languages that include English, Arabic, and Chinese. Once detected, these messages are automatically removed. The algorithm is able to comprehend a message’s context almost as well as humans.
6. Dealing with cyberbullying and distasteful comments
“No one likes you!”
“You are ugly!”
“You’re a loser!”
Social Media has since long been the instrumental platform for people, particularly teenagers to indulge in cyberbullying and Instagram is no exception.
The platform has vowed to fight online bullying by leveraging artificial intelligence techniques that will foresee and recognize any kind of bullying or offensive text on the platform. For the same, the platform has recently launched a new AI feature that works by keeping track of a list of words and phrases which have been reported offensive in the past and then alerting it’s users whenever their captions for a certain photo or video could be considered offensive in order to give them a chance to halt and re-assess their words before posting them.
Around the month of October 2019, Instagram launched a feature termed as “Restrict” that allows the platform’s users to easily shadowban any users that are posting offensive or bullying comments. This means that the comments on the posts of a person who has been shadowbanned by the user will only be detectable to that person.
7. Gather insights to learn and improve
Instagram with its millions of daily shared posts has the potential of becoming a beneficial cultural analysis tool.
For instance, As per a study launched by Cornell University in 2016, a group of researchers aimed at exposing how cultural clothing trends vary across the world, in particular, looking at fashion trends, based on their time and location, between 2013–2016.
Using techniques like face recognition to eliminate irrelevant photos, the researchers designed an object recognition program that could distinguish and recognize items of clothing, for instance, a shirt from a t-shirt or a jacket from a sweater. The program studied and determined what trends were popular in what areas, what clothes were being paired with what, and how the trends had varied within the time period of the research.
Elon Musk’s Tesla Inc, the American electric-automobile manufacturing company has recently been the prey of a large degree of scrutiny and scepticism, in particular regarding its failure to bring to completion its promise of delivering “fully self-driving cars” by the end of 2019.
Firstly let’s talk about what exactly Tesla is about and what areas does the firm deals in. Established in 2003, Tesla was founded on the principle that electric cars are superior, faster and more enjoyable than gasoline cars. The company also claims to lay immense emphasis on striding towards a zero-emission future with less reliance on fossil fuels.
One point that has been the target of much consideration and debate with respect to the firm is whether Tesla is merely an “automobile company” or whether it can also be termed as a “tech company”.
“We develop and deploy autonomy at scale. We believe that an approach based on advanced AI for vision and planning, supported by efficient use of inference hardware is the only way to achieve a general solution to full self-driving.” — stated by Tesla on its official site
Recent AI Tools leveraged by Tesla:-
The Tesla system consists of two AI chips in order to support it for better road performance. Each of the AI chips makes a separate assessment of the traffic situation for guiding the car accordingly. The assessment of both chips is then matched by the system and followed if the input from both is the same.
In case of any discrepancy, a revaluation is done until a safe decision is taken. With the purpose of properly safeguarding the car against failure, it has surplus power as well as data input feeds so that the car can resume working in case of a single unit failure though it’s spare units. Through these abundant features the firm is ensuring that in case of an unanticipated failure, the car will be well equipped to avoid any accidents.
These AI chips have been optimised to run at 2 GHz and perform 36 trillion operations per second, achieving this level of performance by dismissing all generic functions and channelling the focus on only the important ones. Having taken over 14 months of severe research and involvement the chip was designed with Samsung now manufacturing the processor. The chip will be installed in both the new Tesla cars as well as the old models.