Our computer systems are like a safe. They store various important files and personal data which you went to keep secure. That is where you want your computer to be protected from hackers. No hardware or software is absolutely secure. A “real” hacker would be able to penetrate all your defenses if he/she wants to. Even state-funded organizations such as the National Security Agency (NSA) are not safe from this menace. The best you could do is keeping your defenses strong. A few ways you could manage that are:

• Do not back up sensitive data online. If you have sensitive documents or images, it would be best if you keep them off external servers. Instead of backing them up on services like iCloud, Google Drive or Flickr, you should save all sensitive information on an external hard drive that could be accessed only when you are offline. According to CEO and co-founder of Wickr, Nico Sells, all types of perilous websites with malware could put your computer security at risk and give hackers access to your data.
• Two-factor authentication. This application works as a stop-gap when you log into a site using a new device. Apple, Twitter, Microsoft, Google and Dropbox all are equipped with two-factor authentication. Enabling this feature allows the app to send you a notification if you log into your account with a new device, and you would know if someone unwelcome is around.
• Do not link accounts. Many apps work on the basis of your Facebook Login credentials, therefore, often, it is problematic to keep accounts separated. Keeping accounts linked could be troublesome because in 2012, Wired’s Mat Honan’s digital life was hacked and Gizmodo, who had his twitter account linked with Mat Honan’s, also had to suffer the consequences, as both accounts started posting spam. To avoid this peril, you must keep independent accounts for all sites, each with its own unique Login ID and password.

Without any doubt, computer algorithms have played a salient role in shaping our lives. Algorithms are used in a diverse spectrum of activities where data is collected and interpreted. Nonetheless, the approach risks being a potentially job slaying technology as computers can now write news stories. A number of faculties including the Medill School of Journalism have already integrated the approach, with the assistance of companies such as Narrative Science which provide algorithm programs to facilitate the technology.

Currently, Narrative Science’s algorithmic bull pen has the capability of producing news stories which are based on topics of philosophical enquiries. This may range from reports of corporate earnings, to updates of sport contests or a blithe report of the presidential race drawn from facebook posts. A number of niche companies like Forbes, together with other Internet media conglomerates, have employed the services of Narrative Science to generate update articles in sectors such as small cup investments, fast food enterprise and sports too. Surprisingly, the news stories don’t read like they are computer written at all!

Kristian Hammond, CEO of Narrative Science, mentions that the above initiative, facilitated by the buzz of coders and engineers, is only the beginning of an era where the news domain will be characterized entirely by computer generated stories. He predicts that computer written stories will dominate 90 percent of the news domain in 15 years.

Have you ever wondered what is the criterion used by Facebook to show a particular post in your newsfeed? There are about a billion users on Facebook and a substantial number of posts are created every minute with privacy set as public then why do you see the posts from the people most related to you?

Well, there are a number of ways in which the Facebook decides to show you a post. First of all the posts from the people you have added as friends are the most important and they are showed to you, but what if you have a large number of friends like 800-900 or a thousand? Facebook uses an artificial intelligence algorithm which decides which post is more important or interesting for you. It depends on a number of factors some of which are described below:

1: Your Relation with the Person

The first thing to decide the usefulness of a post for you is the type of relationship you have with the person who has posted it. The posts from your acquaintances will show lesser frequently than the ones from your close friends or relatives.

2: Mutual Friends

If you have a large number of mutual friends with someone, it means they are more important and hence the stories involving them are shown with priority.

3: Chat

If you chat with a person more often, it means they mean a lot to you hence their posts and stories show up higher in the newsfeed.

4: Common Interest

If you have some interests in common with a friend, their posts will have more priority and will be shown above the ones from the friends who do not have interests in common with you.

5: Interaction Outside of Facebook

It is decided by the number of photos and posts you are tagged in with a person. The algorithm considers that the people with whom you hangout are the ones you want to know about the most.

6: Page Posts

Page posts and sponsored content is shown according to your interests. For a person having interest in cars, the posts related to cars will be shown higher.

This is the era of artificial intelligence and this science is getting scarily advanced. Most of the times, when we enter a search query in Google, Facebook or YouTube, the required results show up even before we are finished writing the question. There is nothing behind the screens reading our minds in some mysterious pattern. This is pure science and all these things work on algorithms specially designed for the purpose.

While using Netflix, the recommendations for movies are generally the ones we enjoy watching. This is accomplished by an algorithm that instructs the servers to recommend a movie the viewer is most expected to like. This algorithm works by taking into account a number of factors which include, but are not limited to:

• The movies themselves. They are arranged into groups of closely related movies
• The ratings of the movies. If a movie is rated high by a customer who also watched the movie you are watching now, it is likely to show up in the recommendations.
• The combined ratings of a movie by all users. The movies with highest overall ratings are likely to be enjoyed by almost everyone.

The algorithm which does all these functions is called CineMatch algorithm. It has proved to be a very successful algorithm over time. The users watching CineMatch recommended movies have given them 5 stars 75 out of 100 times. Half of the users have rated the movies the same as the previous viewers had rated them. The CineMatch algorithm doesn’t only work by the data already on the servers. For individual users, it also learns from the behavior of the user to better predict a movie the user is expected to be interested in. Recently, Netflix had a makeover of the algorithm to improve its performance. It cost the company around a million Dollars.

Students-researchers at the Georgia Institute of Technology created a computer software that can sort and edit vacation footage and create the ultimate, most picturesque highlights for a vacation reel.

The algorithm developed by two students, Daniel Castro and Vinay Bettadapura under the guidance of Professor Irfan Essa, is a new approach that can analyze video footage for images with almost ideal artistic properties. The way a perfect vacation video is created is by algorithm considering geolocation and composition, then symmetry and color vibrancy in order to decide what is most important and what is least important. Video frames with the highest scores are then processed into a picturesque highlight reel.

Its creators came up with the approach after Bettadapura came back from a vacation driving coast-to-coast across the southern United States. He had almost 27 hours of vacation footage, recorded with a wearable head-mounted camera.

“The data was essentially useless because there was just too much of it,” said Bettadapura. We liked the idea of being able to automatically generate photo albums from your vacation, algorithmically.”

The algorithm turned all of those hours of video into a 38-second highlight reel in just three hours. It was helpful that the wearable camera Bettadapura used captured GPS data, so that the algorithm could sort by geographical location. This reduced the footage to 16 hours. Shot boundary detection further reduced it to 10.2 hours of video. The algorithm then processed for artistic quality and produced an output of the most visually appealing content.

The new video-editing solution can be adapted to user preferences. “We can tweak the weights in our algorithm based on the user’s aesthetic preferences,” Bettadapura said. “By incorporating facial recognition, we can further adapt the system to generate highlights that include people the user cares about.”

The researchers from the University of Rochester have developed an algorithm that helps in detecting the tweets done by a person while drunk. This experiment will help in examining the drinking patterns among the people and will also be helpful in studying various other human behaviors.

The researchers began with selecting the tweets from New York City and rural New York, and sorted them based on various keywords such as “drunk”, “get wasted”, “vodka”, “getting high” etc. These keywords were then used in building an algorithm so that these keywords can be identified by it.

Further, they filtered the tweets selected and sorted by the algorithm through various stringent questions to select the specific tweets, which not only referenced about drinking but also indicated that whether the person was drinking while sending the tweets. This way the algorithm was able to determine whether the person was actually drinking while sending the tweets or was just sending the tweets containing such keywords. Upon the successful completion of a reliable database, the algorithm was fine-tuned so that it could recognize the words and locations that more likely to prove that the people were actually drinking while sending the tweet.

In order to precisely locate the position of the tweeter, the only geo-tagged tweets were used. Further, the users’ home location was determined by checking their location while they sent the tweets during the evening or the tweets containing the keywords like “bed’ or “home”. This addition helped the researchers to determine whether the users preferred to drink at the bars or at the restaurants.

The researchers then combined the both datasets to get an extensive idea of the number of peoples drinking in an area at a particular time. As result of this, they found a correlation among the number of bars and the number of people drinking.

According to the researchers this is just the beginning. Their machine-learning algorithm can track a wide range of behaviors using the actions that people record on twitter such as exercising, playing, shopping and eating etc. Theoretically, anything containing a hash tag on twitter can be tracked.

But there are certain drawbacks associated with this algorithm. Since twitter is more actively used by certain group of people as compared to the others, the data used by the algorithm for retrieving any kind of behavioral information will represent those specific groups. Nevertheless, this algorithm can be very promising in gathering the information about various habits and behaviors in the society.

Dijkstra’s algorithm is an algorithm for finding the shortest path in a network of nodes in a graph (abstract data type). The original Dijkstra’s algorithm found the shortest path between two nodes but now many variants exist. A more common variant fixes a single node as a source node and finds the shortest path from the source to all other nodes in the graph. It thus produces a shortest path-tree. It can also be used to find the shortest node from a source node to a destination node; this is achieved by stopping the algorithm once the destination node is reached. For example, if the nodes of the graph were to represent cities then Dijkstra’s algorithm can be used to find the shortest route between one city and all other cities.

Let the initial starting node be called the initial node. Dijkstra’s algorithm works by initially assigning the initial node a zero value and an infinity value to all other nodes. It also creates an unvisited set and assigns it all the unvisited nodes which are initially all the nodes except initial node. First assign all the neighbors of initial node their tentative distances and then compare their values from their initial values and replace if that value is smaller. In this case the initial value was infinity so it will be replaced. Set the initial node as visited and check the neighbors of the initial node and repeat the above step. In the end the final destination node having the smallest value is selected and its path is made from the initial node.

This algorithms is widely used in network routing protocols, most notable being IS-IS and OSPF (Open Shortest Path First). It is also employed in other algorithms like Johnson’s as a subroutine.

We all have some form of encryption on our devices. We use passwords both as texts and pictures, we can receive alerts through messages and emails while we have secure company accounts that have security embedded in them. But the government wishes to know about certain things when they are investigating a case, which can lead to encryption look bad for both consumers and the law enforcement agencies.

The recent issue in relation to why encryption matters has been outlined during the court order given to Apple. It wants to allow FBI to ask Apple to decrypt the iPhone 5C San Bernardino attackers used. The resulting data is vital towards national security. But there is an issue. Apple has refused to do this. Not because it doesn’t want to, but because it simply can’t.

Technology companies make encryption in order for it not to get breached, not even by themselves, or any in their departments. Apple is now being supported by both Microsoft and Google, who also think the government should not be provided with any Back Doors, which can help hackers understand their systems. If there is a back door to security, it means there is a way to hack into systems and hack data which has been stored hidden from the public and private domain for years now.

All this makes the encryption fiasco look good and bad at the same time. When the government will not get Back Door access to files it wishes to retrieve, it may signal them to use other means. These other means may relate to using bots and hacking languages or viruses, which can help break such encryptions.

To a consumer, it might be satisfying that there is no way to compromise privacy, but it makes the law enforcement agencies look bad. What is surprising is how would the technology companies react when they get hacked or attacked by Chinese or Russians, who pose a greater threat to the national security interests of United States than any other cyber attacking nation. As per Apple, for now there is no technology that FBI can use to break into their systems or given secure access to, while for the law enforcement agencies especially FBI and NSA, it is getting even more complex to keep track and spy on American citizens who may, in turn, become the next wave of attackers like those of San Bernardino.

Robots are becoming an integral part of our lives. Technology is fast replacing manual work either electronically or with the help of robots.  These robots or any computing hardware is just a piece of junk unless they have some algorithm running in the background. We can safely state that algorithms play a major role to understand and assist in the development of the entire world.

However, there is one point that we have often overlooked – the work of humans is not being replaced with these algorithms. In fact, they predominantly help us to ease the growing pressure for building a good future, professionally and personally. One such area where these long but headstrong algorithms can be worth your life is while detecting collision.

Statistically speaking, automobile industry is been aided the most with the upcoming system. One of the studies conducted in MIT and North Carolina School of Law questioned whether robots can be lawyers. To mutate this query, lawyers dug deep in to research only to find out that there are merely few tasks that can be handled by robots. The rest of the evidence that is needed and is of vital importance can be only conducted through researches carried by humans. Although this is right, the algorithm stuffed robots can do more than that.

Algorithms can not only change your life but even save people from deaths. The ones that are designed especially to work for the betterment of collision detection in trucks and vehicles have proved to be a boon to the automobile industry. The sales people, truck drivers and everybody who control the wheels for most of the day have complimented to have such algorithms that can predict collision and accordingly help them to survive.

To an individual, cybersecurity might not look much. As we are not big corporate or hold any national security data, we might not be hacked or harmed. But the outlook for 2016 in terms of personal and national cybersecurity needs to be understood. The United States and governments internationally are working towards making sure cybersecurity works in accordance with their laws and regulations. This includes safeguard of intellectual property, records of employees in both private and government jobs, as well as working out contingencies for any potential cyber war.

As more and more industries use computers to work around their daily workload, cybersecurity makes a compelling case to review. One of the prime examples of why cybersecurity is important can be reviewed on how employees of Sony lost their data due to a cyber-attack. The attack was found out to be have carried out from North Korea, which started through the use of images in emails. Sellers who find vital information and then sell it to the black markets are the biggest threat to our personal data breaches. They use cyberattacks to extract information from us and big corporations and enterprises while using it for their benefits and by use of others who might need important national security leads to make policies. IBM strictly advises its clients to look for measures to have protection from such cyberattacks.

Another prime example was the cyberattack JP Morgan Chase encountered last year. More than 76 million customers were exposed to a data breach while a lot of company data was lost. JP Morgan has since started to invest more than $500 million in protecting its data while employing teams from law enforcement agencies to look after BIR systems. There is a need to address cyber security at an individual level too. Having secured Wi-fi networks at home, or using encryption in data and computer files is a possible means to stay safe and secure.