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by Joanne Jiang

AI, IoT, advanced analytics and blockchain are some of the trends driving competitive advantage for supply chains.

Olay Skin Advisor is a mobile app that relies on machine-learning algorithms to analyze skin care needs. The app performs a facial analysis from a consumer’s no-makeup selfie and recommends products based on personal data and best practices from skin care experts. The artificial intelligence (AI)-enabled app also collects buying behavior data directly from the consumer and uses that data to determine the demand for and recommend specific products.

“Supply chain leaders must assess their company’s risk culture to determine their readiness to explore and adopt emerging offerings”

Similarly, FlavorPrint, an AI-based platform introduced by McCormick spinoff Vivanda, determines what is called a “flavor DNA” — a digital taste identifier that matches consumers to food items. Through this direct customer engagement, FlavorPrint is sensing demand by better understanding customer preference. Furthermore, it can make this information available to the extended supply chain — suppliers, food manufacturers, content publishers — for better demand response and creation.

With the emergence of disruptive supply chain technologies like AI, supply chain leaders must determine the implications of those trends to enable the future of supply chain and operations.

“Supply chain leaders must assess their company’s risk culture to determine their readiness to explore and adopt emerging offerings,” advises Christian Titze, research director at Gartner. “If in doubt, consider piloting small projects to determine whether the potential benefit of the technology trend is worth the risk and required investment in new skills, capabilities and services.”

Gartner has identified eight strategic technology trends for supply chain and how they can provide a competitive advantage.

Artificial intelligence

AI carries great potential to revolutionize supply chain processes. The ability to apply AI to enhance, and even automate, decision making, reinvent business models and ecosystems, and remake the customer experience could make many other emerging technology trends redundant. However, although current AI solutions can find patterns and predict future scenarios, they still lack decision-making abilities. Combining pattern capabilities with more advanced prescriptive capabilities will therefore be critical to widespread supply chain uptake, enabling users to dedicate their skills to higher-order use cases such as strategic network design or capacity planning.

Advanced analytics

Advanced analytics enable companies to proactively take advantage of future opportunities and mitigate future adverse events. Prescriptive analytics can improve decision making in functional areas like supply chain planning, sourcing, and logistics and transportation, and can be deployed to improve end-to-end supply chain performance. Processes that previously relied on human judgment can be powered with predictive and prescriptive analytics that could have a significant impact on future demands for supply chain talent.

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General issues:  There are many issues which are related to the emergence of Artificial Intelligence. In this essay,  I will focus on those  I  consider  significant  enough  to  be  discussed.  A  very important issue nowadays is unemployment. However, in developing countries, it is more severe  as  compared to  developed countries.  Nonetheless, as  the new  technologies  and especially Artificial Intelligence are emerging out by leaps and bound and contributing in the automation of processes at industrial and corporate levels, some argue that the state of employment is at risk, however, others argue that it will bring innovation in products and services and hence new possibilities of employment. Another important issue is related to morality, wherein the concern is related to the ethics of intelligent machines which bring up the issue of safety. A lot of major enterprises (like Google, Facebook, Amazon, IBM, Baidu etc.) are in the race of developing intelligent machines but how can it be ensured that they will not be used for warfare purposes. Another issue is related to the competence of the machines. Up to what extent one can count on the intelligence of these machines when it really comes to safety and security? The recent  accident of  Tesla’s  autonomous car is the perfect example which suits this position. 

There was a very good scenario presented in the science-fiction movie called iRobot when a robot saves a police detective from  a car  crash while  leaving a  girl to  die as  according to  machine intelligence  her survival was statistically less likely than that of the police detective:  Another more severe issue is related to the technological singularity which can be seen as the  point  in  time  when  machines  will  achieve  Human-Level  Machine  Intelligence (HLMI). Nick Bostrom said at TED that "machine intelligence is the last invention that humanity will  ever need to make". Consequently, it is really important now to think intensively about the kind of world we are building right now. We have to make sure that at  the  technological  singularity,  these  smart  machines  will  safeguard  humanity  and preserve its values and not develop their own values.  Many technological evangelists, inter alia Elon Musk, Stephen Hawking, and others are warning of AI, even though the actual threats they see vary. This essay will do justice to the main  issues arising and analyse to which extent they are realistic and how they are taken care of throughout the development process.   

Therefore, the Hypothesis here is:  Keeping all these issues in mind, the development of AI is debatable in my opinion. If we are going to continue developing AI, then we have to make sure to eradicate all the issue we have now in our mind. All these issues can be tackled if we make a global framework of rule and regulations which has to be followed while developing AI in order to make sure that it proves to be a boon for humanity rather than a bane.  The  hypothesis  of  this  essay  is,  therefore:    “If we  keep  ignoring  the  social  bugs  of Artificial Intelligence (AI), it could be a serious threat to humanity”.    

Methodology:  In order to test my hypothesis, I will use the analysis methodology wherein a special focus will be put on interdisciplinary analysis methods, combining economical prognostics, the historical development of technologies, and systematic description of the various threat scenarios.    

Issue related to the application are mainly concerning the development of a framework which helps  us to  eliminate the  problems which  are connected  to the  rapid development  of  AI. However, some of the domains where the results can serve might include the following: - AI research framework: Creating  a  system of  research and  development which  can help us fight with existential risk from Artificial General Intelligence (AGI). One such system which is recently established by Tesla CEO Elon Musk is known as OpenAI.  - Economical and Governmental agents: Understanding the process of AI development while it is still adjustable, becoming aware of issues arising and actively taking part in designing the future with policies and informed future planning.  - Legal Aspects: Creating a legal framework in order to avoid all the consequences and make sure no harm  is done to society and in turn to humanity. Such a legal framework is becoming a need as AI development is rapidly moving on its way.   

Verification 

In the recent years, there have been several advancements in the development of AI. For many years it  was used for various internet based activities e.g. advertisement, web searches etc. Now it is gaining more commercial fame than ever before. The current state of the art in AI research  makes it  more  competent in  some  domains than  humans,  however, it  is  part of narrow  artificial  Intelligence  (also  called  Weak  AI)  which is  more  focused  on  a  narrow domain of problems e.g. iPhone Siri. In 2015, several milestones achieved in the research and development of AI gave rise to Artificial General Intelligence (also known as Strong AI) and this  kind of  AI can  be applied  all kind  of problems.  In cognitive  science, intelligence  is defined  in  many  ways  which  include  one’s  capacity  for  reasoning,  logic,  understanding, planning,  problem solving,  self-awareness,  and  emotional knowledge  etc.  A Human-level intelligent machine should have an ability to pass several tests; one of such tests is the Turing test.  However, there  is no  perfect  test which  can  prove  a  machine perfectly  human-level intelligent.   

The current state of AI

If we look at the current state of AI, the pace of evolution of artificial intelligence is speeding up. NIPS (Neural  Information Processing Network) conference  is one  of the  most famous conferences in the field of Machine Learning & computational neuroscience. It is the same conference  wherein  2013,  Facebook  CEO,  Mark  Zuckerberg  announced  to  form  an  AI laboratory and a start-up called DeepMind boastfully displayed an AI which can easily learn to play computer-based games. Afterwards, DeepMind  was acquired by  Google.  However, Artificial Intelligence is not new, it was first coined by the American scientist John McCarthy in 1955, who is also considered co-founder of the field Artificial Intelligence. The term ‘Robot’ was first coined by Karel Čapek in  his play R.U.R (Rossum’s Universal Robots) in 1921. So research in the field of AI has been done since decades, but the conditions were not appropriate for AI to flourish. Nowadays, we have cloud computing to store torrents of data remotely & inexpensive neural network technology which is crucial in learning, which was very expensive back then. Given the fact conditions for AI nowadays are right, largest companies in tech-industry e.g. Google, Facebook, Microsoft and IBM have dived into AI research, where they see a huge potential. Below are some of the images which empirically show the growth in AI domain in recent years: 

            Fig. 1: Error rate fall over recent years (Clark, 2015)  

Since  Google’s  acquisition  of  DeepMind,  they  are  working  intensively  on  making its  AI better at playing old Atari games. Below is the bar chart which shows the growth in accuracy of their AI:         

Techniques (Deep Learning) & Companies (IBM, Google, Facebook, Microsoft, OpenAI):  Deep  Learning  is  one  of  the  most  growing  research  areas  in  the  domain  of  Artificial Intelligence. It  facilitates to  build and  train neural  networks.  Neural  networks provide the foundation for learning and decision making in AI systems. Hence, deep learning lays down the framework for the Strong AI  also  known as Artificial General Intelligence. In order to create human-level machines intelligence, it is important to achieve advancements in Strong AI  because  well-structured  problems  e.g.  chess,  are  solvable  by  AI,  while  ill-structured problems  e.g.  real  world,  are  still  relatively  difficult  for  AI  like  the  Winograd  Schema Challenge (an alternative to the Turing test based on contextual language understanding and common-sense knowledge etc.). After putting a lot of effort to develop narrow AI based intelligence, now tech-industry giants are going towards Strong AI, which is the generalization of narrow AI. Unlike Narrow AI, Strong AI  is focused on  a problem domain  which can consist  of any problem  in the  real world.  Deep  learning is  the  basis  for  Strong  AI  and  tech  giants  like  Google,  Facebook, Microsoft, and IBM are investing in the research and development of Deep Learning. Each of these giant companies has their own laboratories, where they carry out the state of art research in AI and publish it every year for the academic community. In 2015, Google published their AI system (in a scientific journal called Nature) that learns and plays video games on its own without directions. Facebook built an image recognition system which can recognize images automatically and can interpret them for blind people. Microsoft boastingly presented a new skype based AI system which can translate from one language to another automatically while IBM indicated AI as one of its highest potential growth areas. IBM is working on its well-known AI system named Watson, who was the winner in the Jeopardy! competition in 2011. Companies like Google have  even started utilizing  Deep Learning in their current running projects to improve their services. Below is the empirical representation of the usage of deep learning into their projects:           

IBM developed  Watson up  to the  extent where  it defeated the  world’s  best  player in  the Jeopardy game. Yet, from an intelligence point of view, it is still a narrow problem-solving domain. IBM achieved this state of AI by combining two separate research areas of AI named natural  language understanding  and statistical  analysis  of unstructured  data.  Now IBM  is diving into the deep learning research to be added to the commercial version of Watson. IBM made  some  of  the  features  of  Watson  available  to  the  developers  via  its  Cloud  API (Application Programming Interface). IBM added three new deep learning based features to Watson  cloud  API  which  includes  translation,  text-to-speech,  and  speech-to-text.  Baidu, nicknamed as “China’s Google”, has also set up its AI lab in Silicon Valley -- and hired one of the most prominent figures in AI, a former employee of Google and an associate professor at Stanford – to compete with other players in AI research.  However, there is a group of technocrats who is concerned about the future of humanity as AI paced its advancements at a rapid rate. These technocrats and business magnets came together and introduced a new initiative called OpenAI – a non-profit AI research company. According to OpenAI’s official website they believe AI should be an extension of individual human wills and, in the spirit of liberty, as broadly and evenly distributed as possible. OpenAI recently released  a  tool  named  OpenAI  Gym  which  provides  a  basis  to  compare  different reinforcement learning algorithms which provide machines a mean to learn via positive and negative feedback. 

As AI research is progressing  with  a fast  pace, a  lot  of companies are  looking forward to utilizing its power to improve  their services. The  largest U.S.  retail company, Walmart,  is trying to build robotic shopping carts, while Amazon is developing robots for its fulfilment centres that will serve the delivery between stacker and pickers.          

The automobile industry is investing a vast amount of money to embed AI into cars. In fact, Tesla Motors  has already introduced  AI into their  cars which  enable  auto-pilot mode  and other  lane changing  features. Toyota  is investing  billions of  dollars to  combine  Artificial Intelligence  with  Big  Data to  facilitate mobility  assistance both  in &  outdoor driving  for people who are less confident to drive. It’s a big opportunity for the industries who are leaders in AI to build self-deriving cars. Hence,  the tech giants Google  and  Apple are investing a huge amount of money to dive into this market.  Google  has recently  launched  an open-source  version  of its  Artificial  Intelligence engine named TensorFlow. Tensor came out of ‘Google Brain’ project which is used to apply various neural network machine learning algorithms to various product and services. Google uses this engine in  various product  and services.  Now researchers  outside of  Google  are using this engine to test  their algorithms. Hence,  there  is a huge potential  in this  technology for  the usage at various commercial levels.   

Next steps of AI

The next steps in AI mostly include generalizing the intelligence and create as many use cases as possible, which eventually can be converged into HLMI. OpenAI wants to further advance AI in a way that benefits society as a whole and is freed from the need of generating revenues. As  OpenAI has  already  made its  reinforcement  learning framework  public,  it will  create possibilities for other companies to create different use cases and contribute to the AI research as an open source project.  Other companies have also started to make use of AI technology into different domains. One such example is Turing Robot, a Chinese company which is behind the HTC’s Hidi, a voice assistant. Turing Robot offers Voice recognition and natural language processing for a wide variety  of  applications  including  Bosch’s  car  system  and  Haier’s  home  appliances.  Now Turing Robot is focused on developing Turing OS for service bots. Another use case is being developed by Amazon, which is working on developing Alexa in the direction of recognizing emotions. Amazon is making significant advancements in Alexa – which is a virtual helper which  sits  inside  the  voice-controlled  appliances  offered  by  Amazon.  While  people  get irritated from the repeatedly wrong response from voice assistances like Google Now, Siri, Hidi  etc., Amazon  focuses on  emotion recognition  from the  voice  tone  and  enable voice assistance to offer an apology for a wrong response. Similarly, a silicon valley based company named Vicarious is developing an entirely a new way of information processing akin to the information  flow  in  human  brain  which  they  believe  to  help  machines  to  become  a  lot smarter.  Toyota is  developing  a  system which will  predict where  you are going  before you tell  it. Toyota has recently announced its new subsidiary named Toyota Connect which will facilitate to collect torrents of data every day and the battle to mine the data already been started. Thus,  in the  future,  your AI  will know  more about you  than  you do. A  start-up  called Brain  of Things is developing a smart home which they named ‘robot home’. These robot houses keep an eye on each activity of the inhabitants -- whether they are watching a movie, sleeping or doing something else. There are lots of advancements made in different domains all using AI technology while developing it further in parallel to achieve their desired results.  

Problem related to AI  

Artificial  Intelligence  has  impressive  capabilities  today  but  they  are  narrow  in  nature. However, as researchers are fighting to widen up those capabilities to make it as general as possible, it seems that AI will eventually reach HLMI which then will facilitate machines the ability to solve any intellectual task which a human can solve. Looking into the future from here makes it difficult to figure out, how much benefit HLMI can bring to society and it is legitimate to ask, how much harm it could bring to society if we build or use it incorrectly. In the near term, automation of services is also going to impact on employment and AI is going to play a major role in making that possible which apparently seems to bring more benefits to big enterprises rather than to society as a whole. Considering for a moment what will happen when, in near term, we have a reliable driverless car system. Thinking about all the drivers --whether they are Uber drivers, train drivers, plane pilots or ship  captains  -- how long will those  jobs be held  by humans?  Besides, our  dependency  on AI  based  services like  using navigation, voice assistance, etc. is also putting our privacy on the verge. There are many such issues which are connected to AI and its development which nowadays are in the debate. 

Once  AI  reaches human-level  intelligence,  further  development  of self-optimizing  AIs  is unpredictable.  The  output  will  then  no  longer  be  approvable  by  humans  for  errors  and conclusions drawn might be beyond human understanding capacities or even beyond human ethics. The main issues here are problems with the agency, where autonomous machines will need to become a legal entity like companies at one point – independently of the question if a code (algorithm)  can  be  fined  in  the  end.  This  connects  to  the  possibility  of  failure  and responsibility of such AIs. The second main issue is formed by moral implications, especially since machine learning does not necessary include human teachers anymore and, if not asked to do so, AIs will not necessarily focus on learning what humans count as valuable acts. [6] Prof. Stephen Hawking -- one of  Britain’s  prominent  scientists -- warns that our efforts of creating a thinking machine pose a threat to our very existence. He said that the development of the superhuman intelligence could spell the end of humanity.

Internet of Things (IoT)

Adoption of the IoT is growing in select supply chain domains, but rarely as part of a complete end-to-end supply chain process. One exception is the air and defense industry, where airplanes have thousands of sensors and data is leveraged in the extended supply chain. Other potentially impactful supply chain use cases are in preventative maintenance, sourcing, manufacturing, logistics, demand management and services. These include improved asset utilization, higher uptime through remote monitoring and maintenance, improved customer service by better understanding customer behavior and needs, and proactively responding to and shaping customer demand.

Intelligent things

Current supply chain use cases for intelligent things — such as autonomous mobile robots and autonomous vehicles — are mainly targeted at defined scenarios and controlled environments, such as in warehouses. Intelligent things will make their initial business impact across a wide spectrum of asset-centric, product-centric and service-centric industries. As a result, the ability for organizations to assist, replace or redeploy their human workers in more value-adding activities will potentially create high, even transformational, business benefits.

Conversational systems

Conversational systems — most recognizably implemented today in virtual personal assistants (VPAs) and chatbots — are taking interaction to the next level with the addition of conversational commerce. Not only can they handle discovery questions and offer solutions without any human agent involvement, conversational systems can enable transactions, handle payments, ensure delivery and provide customer service.

Robotic process automation

Robotic process automation (RPA) allows supply chain leaders to cut costs, eliminate keying errors, speed up processes and link applications. For example, an organization may want to work with structured data to automate an existing manual task or process with minimal process re-engineering or to avoid major system integration projects or specific new major application deployments.

Immersive technologies

Immersive technologies such as virtual reality (VR) and augmented reality (AR) allow supply chain businesses to enhance employee and customer digital experiences. Gartner estimates VR will reach mainstream adoption in the next two to five years, with AR going mainstream in the next five to 10 years, but these technologies are already in use in a variety of industries. These include enhanced repair and maintenance capabilities in manufacturing, logistics and warehousing and better purchasing choices for customers leveraging product visualization or store layout and planning.

Blockchain

Certain highly decentralized supply chain management functions such as smart contracts or traceability and authentication are prime candidates for blockchain. Multiple business use cases are yet to be proven, but some early pilot projects have emerged that are experimenting with the potential of blockchain for supply chain. For example, blockchain is being used to track the movement of diamonds from mining to retail stores by developing a digital record that includes the unique attributes, including color, carat and certificate number that can be inscribed by laser into the stone.

AI and Humans

Moral Issues: As soon as AI is able to compete with humans, it will not only lead to a fight for jobs on an economical level but maybe even intrude human relationships in the way that an AI-friend will only focus on its  owner’s needs, whereas a  human relationship flourishes  through the exchange of  favours  (e.g. portrayed  in  the  movie “Her”).  Another interesting scenario has been portrayed in the very recent movie called Ex-Machina, where a humanoid robot named Ava who already passed a simple Turing test and eventually shows how she can emotionally manipulate  humans.  Ultimately,  the  question  arising  here  is  what  happens  when  our computers get better than we are in different areas of life.  Economical Impacts: A recent bid for the acquisition of a German robotic company Kuka by a Chinese company called Midea Group was $ 5  billion. Kuka is  one of the  world’s  largest robotic companies. China is famous for low-paid migrant labour and Chinese enterprises want to automate the manufacturing process because they do not see  any point  to  rely on such a huge low-paid migrant  labour.  According  to  International  Federation  of  Robotics,  China  is  the  largest importer of robots. The  IFR’s calculations show  that  China  has  326  robots  per  10000 workers while the US and South Korea have 164 and 478 robots respectively for the same number  of workers.  Thus, enterprises  are seeing  a  lot  of  potential in  automation of  their processes but  it will  have a  negative impact  on employment. 

Hello to our lovely followers!

{ Photography by Lea Simmons at Bear Flag Farm, shared from www.fotavo.com }

Today, we decided to change things up a bit. In this two-part series, not only will we be giving our brides some tips on designing wedding invites, but we will also be showing you how to make 3 DIY invitations perfect for any occasion, not just weddings! These cards are perfect for birthdays, baby showers, company banquets, and practically every event.

{ Photography by Lea Simmons, shared from www.fotavo.com }

But before we get to that, the logistics:

- You should be ordering 20-25% the amount of paper and materials you need, so you have enough for practice and any mess-ups.

- Some factors to keep in mind are: 1) font style 2) font size 3) inks 4) alignment. The smallest details can create the biggest differences!

- 5x7 is a good universal size for invitations you can easily find envelopes for. Keep in mind that square invites will cost more to mail!

- Make sure to have a good pair of scissors or paper cutter on hand, as well as a ruler.

{ Decorations from Peony and Thistle, Photography by Sabrina Park, shared from www.fotavo.com }

Now for the embellishments!

fabrics - make pretty ruffles, showcase your personal style

lace / ribbons - add a classic touch of elegance (especially organza!)

buttons / beads - add a playful taste of fun to your invite (keep in mind, this may require patience and extra postage)

stamps - inexpensive way to personalize your invites!

For example:

{ Note: all the following photographs are shared from www.craftaholicsanonymous.net, and also from our Pinterest at http://www.pinterest.com/fotavo/ }

When it comes to wedding planning, there is no doubt finding the perfect dress is one of the most crucial decisions a bride needs to make. But before you dive headfirst into piles of ruffles and sequins, take a few moments to first identify your body type and find the dress shape that will best flatter your body on your big day.

If your hips and shoulders are similar in size, with a well-defined waist

You have an hourglass figure! To play up your figure, we recommend a sheath gown, a ball gown, or a mermaid-style dress. These classic and timeless styles create a silhouette of feminine elegance -- perfect for any bride's style.

{ Photography by John Schnack, shared from www.fotavo.com }

If your shoulders, waist, and hips are aligned

You have a rectangular, athletic silhouette! To add curves to your figure, your best bets are empire gowns and A-line gowns. These dresses create the illusion of an hourglass shape by cinching in at the waist and flowing out to the ground gracefully.

{ Dress by Morilee, shared from www.fotavo.com }

If you have a wider lower body than upper body

You have a pear-shaped silhouette! A classic A-line gown will draw attention to your upper body, especially if paired with a spaghetti-strap top or V-neckline. Note: Tougher fabrics such as satin and tafetta are especially beautiful on pear-shaped bodies.

{ Dress by Amy Kuschel, shared from www.fotavo.com }

If you have broader shoulders, narrower hips, slim legs and thighs, and a slightly larger abdomen

You have an apple-shaped silhouette! Look for a dress that nips in at your waistline and flares to the ground, creating a gentle A shape. We recommend textured dresses for your body type, such as ruche or lace, that will create a snug corset-like effect. Go for deep V-necklines as well, but make sure to avoid mermaid-style dresses which will only emphasize your widest areas.

{ Dress by Stella York, shared from www.fotavo.com }

Remember, your body type is not indicative of what dress suits your figure the best! A key component of wedding dress shopping is physically trying on dresses in salons and consulting dress experts before making a final decision. Most importantly, however, choose a dress YOU feel confident walking down the aisle with; even the most beautiful wedding gown in the world is no match for a radiant smile!

All photos and tips are shared courtesy from Fotavo, a visual platform aiming to simplify the bride and groom's tasks of planning their dream wedding by providing creative inspiration and a supportive wedding community.

For more beautiful dresses, visit www.fotavo.com!